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The Crowd in Requirements Engineering: The Landscape and Challenges
... Crowdsourcing [43], [48], [65], [70] 4 ...
... Focus Group [21], [27], [43], [47], [48], [54], [65], [76], [89] 9 ...
... Interviews [21], [22], [27], [37]- [40], [43], [44], [47], [48], [53]- [55], [65], [66], [69], [70], [72]- [74], [76], [78]- [82], [84], [85], [89]- [112] 53 Introspection [22] 1 Laddering [21], [22], [64] 3 ...
p>Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a literature review to discover methods for understanding business processes and eliciting requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges with methods for eliciting requirements in this context.</p
... Crowdsourcing [43], [48], [65], [70] 4 ...
... Focus Group [21], [27], [43], [47], [48], [54], [65], [76], [89] 9 ...
... Interviews [21], [22], [27], [37]- [40], [43], [44], [47], [48], [53]- [55], [65], [66], [69], [70], [72]- [74], [76], [78]- [82], [84], [85], [89]- [112] 53 Introspection [22] 1 Laddering [21], [22], [64] 3 ...
p>Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a literature review to discover methods for understanding business processes and eliciting requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges with methods for eliciting requirements in this context.</p
... From its onset, CrowdRE was positioned between existing approaches, including Market-Driven RE [4], which proposes the use of instruments from market research such as questionnaires to obtain user requirements, typically from known stakeholders. In 2017, Groen et al. [2] argued that CrowdRE distinguishes itself from Market-Driven RE in two respects. First, CrowdRE can be applied continuously over the constant influx of user feedback, while in Market-Driven RE, surveys are typically conducted in discrete phases and end whenever the strategic information need has been satisfied. ...
... User opinions can be obtained from social media and car-specific forums. Groen et al. [2] describe a scenario in which online user reports of problems are compared to usage data in order to identify recurring problems with a particular model faster, enabling preventive measures (callbacks) and guidance for car workshops to detect and resolve the issue quicker. Although this feedback is not necessarily related to the (embedded and information) software systems of the car, it is still of interest to CrowdRE to help vehicle manufacturers gauge how well certain innovations are received, or whether the vehicle appeals to its target audience; questions that market researchers at a car manufacturer may have, but which essentially validate whether stakeholder requirements regarding certain features have been met. ...
... But a central question in CrowdRE is how the crowd -i.e., the users of a product or service (cf. [2], [3]) -share their opinions over online platforms. Through QR codes or NFR chips, even the interaction with the company through its own platforms could be more closely monitored. ...
In recent years, market researchers have increasingly adopted automation, among other things to understand what users want from their product or service and how satisfied they are overall or with specific features or changes. Crowd-based Requirements Engineering (CrowdRE) is concerned with similar questions and employs requirements engineering (RE) methods to answer them. This suggests that the boundary between the fields of market research and CrowdRE has become blurred. This in turn raises the question: When is something still CrowdRE, and when has it become market research? This is an important question because it may require the community to rethink how industry might perceive CrowdRE in a wider context, and how future research should be framed. This problem statement paper explores this question along several real-world scenarios to trigger discussions in the community regarding possible solutions.
... Academic literature has conceptualized data-driven RE as enriching classical methods and feedback sources for RE with novel feedback data and software usage data (e.g., Maalej et al., 2016;Franch et al., 2017). Scholars have made the case that successful data-driven RE is becoming possible as increasingly powerful technologies for big data analytics can be used to analyze usage data and feedback sources such as social media and online reviews (e.g., Maalej et al., 2016;Groen et al., 2017). Prior work has looked at data-driven RE mainly from conceptual and technical perspectives in specific areas (e.g., Maalej et al., 2016;Oriol et al., 2018). ...
... In sum, data-driven RE in ECSD aims to bridge the vendor-user gap by (i) opening new channels for feedback from the user base to the enterprise software vendor and its automated analysis and (ii) analyzing user interactions with cloud software products. Maalej et al. (2016) describe data-driven RE as a user-centric, real-time, and proactive decision-making approach in RE. Groen et al. (2017) propose data-driven RE based on crowdsourcing in which end-user feedback turns RE into a semi-automated, participatory effort. They further highlight the potential of conjoint elicitation and aggregation of usage data with multimodal feedback (Groen et al., 2017). ...
... Groen et al. (2017) propose data-driven RE based on crowdsourcing in which end-user feedback turns RE into a semi-automated, participatory effort. They further highlight the potential of conjoint elicitation and aggregation of usage data with multimodal feedback (Groen et al., 2017). This potential is also asserted by Bosch and Olsson (2016) who address the problem of getting accurate and timely feedback from customers for product management decisions. ...
The ongoing transition from on-premise to cloud solutions in the enterprise software market entails important changes in how software vendors interact with their users. Where user involvement has traditionally been a challenge, increasingly large amounts of usage and feedback data now allow for datadriven requirements engineering (RE). Prior research has provided conceptualizations of data-driven RE, introduced initial technical prototypes, and shed light on the general social interactions in RE. However, extant research lacks a comprehensive perspective on the socio-behavioral elements of datadriven RE for enterprise cloud software development and empirical insights. We obtained access to a large enterprise cloud software vendor for a revelatory single-case study and conducted interviews within seven different cloud software products. We demonstrate how data-driven RE affects knowledge transfer, mental models, and trust between stakeholders. We observe a shift from a stakeholder-centric towards a more user-centric RE process by opening new direct requirements elicitation channels between the users of a software and the development organization. Our study reveals that the data-driven approach holds much potential to scale and accelerate RE for enterprise cloud software, but there are still numerous obstacles to overcome in order to achieve high levels of context-awareness, continuity, and automation in RE.
... Crowd-based Requirements Engineering (CrowdRE) is an emerging paradigm for Requirements Engineering (RE) that promotes the active involvement of a "crowd" of stakeholders, including the current and potential users, of a software product [1]. CrowdRE expands the reach of established RE approaches [2], which involve a selected sample of the stakeholders, extending the notion of market-driven RE [3,4] toward the democratic participation of users in RE [5]. ...
... So far, CrowdRE research has mainly investigated requirements elicitation [6]: "the process of seeking, uncovering, acquiring, and elaborating requirements for computerbased systems" [7]. CrowdRE researchers [1] have proposed two approaches for complementing existing elicitation techniques with requirements-related feedback from the users 1 : (i) in pull feedback, the crowd is requested to express their needs and wishes through a dedicated feedback channel; and (ii) in push feedback, the users initiate the process of providing feedback, e.g., by sending feedback through an app store. ...
... Many of these researchers co-authored a landscape paper [1] that distinguishes between two main approaches to CrowdRE: (i) pull feedback concerns the provision of a feedback channel for the crowd to formulate their ideas; and (ii) push feedback denotes user-initiated feedback processes, e.g., through the authoring of reviews in an app store. Both streams of user requirements are then analyzed by a product team in order to further improve the software system at hand. ...
Crowd-based Requirements Engineering (CrowdRE) promotes the active involvement of a large number of stakeholders in RE activities. A prominent strand of CrowdRE research concerns the creation and use of online platforms for a crowd of stakeholders to formulate ideas, which serve as an additional input for requirements elicitation. Most of the reported case studies are of small size, and they analyze the size of the crowd, rather than the quality of the collected ideas. By means of an iterative design that includes three case studies conducted at two organizations, we present the CREUS method for crowd-based elicitation via user stories. Besides reporting the details of these case studies and quantitative results on the number of participants, ideas, votes, etc., a key contribution of this paper is a qualitative analysis of the elicited ideas. To analyze the quality of the user stories, we apply criteria from the Quality User Story framework, we calculate automated text readability metrics, and we check for the presence of vague words. We also study whether the user stories can be linked to software qualities, and the specificity of the ideas. Based on the results, we distill six key findings regarding CREUS and, more generally, for CrowdRE via pull feedback.
... A number of empirical studies have shown that app store reviews contain valuable feedback and opinions, such as users' requirements and documentation of their experiences using the app and its features (Maalej and Pagano 2011;Seyff et al. 2010). Crowd-Based Requirements Engineering (Crow-dRE) is an approach that automates users' feedback analysis to derive validated requirements (Groen et al. 2017). Using apps reviews as a crowd-based source instead of conventional approaches (e.g., surveys or interviews) overcomes the challenge of engaging a large number of users with different socio-demographics (age, race, ethnicity, geographic area, etc.). ...
... Receiving feedback from various socio-demographics is essential as the apps audiences are mostly global. Utilizing the crowdbased feedback enhances the quality of the decision support process, hence, leading to a better software quality (Groen et al. 2017). ...
Apps reviews hold a huge amount of informative user feedback that may be used to assist software practitioners in better understanding users’ needs, identify issues related to quality, such as privacy concerns and low efficiency, and evaluate the perceived users’ satisfaction with the app features. One way to efficiently extract this information is by using Aspect-Based Sentiment Analysis (ABSA). The role of ABSA of apps reviews is to identify all app’s aspects being reviewed and assign a sentiment polarity towards each aspect. This paper aims to build ABSA models using supervised Machine Learning (ML) and Deep Learning (DL) approaches. Our automated technique is intended to (1) identify the most useful and effective text-representation and task-specific features in both Aspect Category Detection (ACD) and Aspect Category Polarity, (2) empirically investigate the performance of conventional ML models when utilized for ABSA task of apps reviews, and (3) empirically compare the performance of ML models and DL models in the context of ABSA task. We built the models using different algorithms/architectures and performed hyper-parameters tuning. In addition, we extracted a set of relevant features for the ML models and performed an ablation study to analyze their contribution to the performance. Our empirical study showed that the ML model trained using Logistic Regression algorithm and BERT embeddings outperformed the other models. Although ML outperformed DL, DL models do not require hand-crafted features and they allow for a better learning of features when trained with more data.
... Paying attention to the direction of CrowdRE research is critical for companies to improve requirements elicitation [8], [9]. The ability to vastly increase the amount of feedback considered [10] is extremely valuable. ...
... User feedback from the 'crowd' is then transformed into requirements either through manual content analysis [11] or through automated approaches [12]. Groen et al. argue that CrowdRE can address the limitations of traditional RE methods, such as the limited scope and representation of user feedback [9]. By harnessing the collective intelligence of a crowd, organizations can utilize CrowdRE to identify and prioritize user needs and improve user engagement for their product [13]. ...
The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.
... Researchers also emphasized the difficulties in eliciting BPAS, including (1) lack of process standardization, (2) process variability, (3) deprecated documents that do not faithfully represent the process performed [6,9,11,40], (4) lack of knowledge of the vocabulary used by participants in the organization [18,[45][46][47][48][49], and (5) lack of engagement of the stakeholders involved in the process [21,45,[49][50][51][52][53][54][55][56][57]. ...
... Ref. [46] introduced some techniques to elicit requirements in the health care domain. Some researchers employed artificial intelligence (AI) techniques such as K-nearest neighbor (KNN) [51,72,73], data mining [74], and natural language processing (NLP) [74][75][76][77][78] to automate or improve the requirement elicitation, especially to support a crowdsourcing technique [56]. Other methods transcribed the domain documentation into requirements [98,103]. ...
Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a systematic literature review to discover methods to understand business processes and elicit requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges and opportunities. The challenges of developing a business process automation software include dealing with business processes, meeting the needs of the organization, choosing the right approach, and adapting to changes in the process during the development. These challenges open opportunities for proposing specific approaches to elicit requirements in this context.
... Data-Driven Requirements Engineering (DDRE) provides methods and techniques at support of software developers and analysts willing to exploit user feedback for eliciting, prioritising, and managing requirements for their software products [1]. RE research has devoted huge attention to automating DDRE, but several challenges remain to be addressed in order to better integrate DDRE into a continuous software development process, as discussed, for instance, in [2][3][4]. The opportunity to further research on how to leverage user feedback not only at requirements elicitation, but also at other stages of the software requirements life-cycle is highlighted in [4,5] along with the need to enact traceability of feedback to software design artefacts. ...
... Specifically, the measure M1 evaluates the goal RG1.3 by providing measurements on the app functionality corresponding to the task T1.3a that operationalises the goal via the means-ends relationship. Figure 3 depicts the meta-model of the GO+GQM method, which is composed of three main parts: the left part concerns the concepts taken from GQM and is related to the 2 In particular, we inspire to the software application used in [27]. ...
According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.
... In the context of crowd requirements engineering (CrowdRE) [12], researchers have started to investigate the potential benefits of performing feedback analysis to enable validating, verifying, or identifying requirements for a product and identifying potential bugs. Stade et al. [13] presented the FAM E framework for collecting feedback and monitoring data to support requirements elicitation. ...
... In addition, it is mentioned that the way feedback is requested, as well as the simplicity and usability of the feedback process, are important to motivate users to provide more feedback [19]. M oreover, a user's motivation is strongly influenced by system improvements based on their feedback [12] [19] [20]. All publications discuss objectives for collecting in-app feedback. ...
Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool.
... With the recent widespread availability of NL content relevant to RE, such as feedback from users in app stores and social media, and developers' comments in discussion forums and bug tracking systems, we have observed a rising interest in using ML techniques to support data-driven RE [38] and crowd-based RE [39]. These areas aim to leverage information available from stakeholders' implicit and explicit feedback, including diverse sources as app reviews [40], issue tracking systems [41], Twitter [42] or user fora [43], to improve RE activities such as requirements elicitation and prioritization. ...
... 38 Stemming A crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. 39 Lemmatization Use a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. 40 Stop-Word Removal Words which are filtered out before or after processing of natural language data (text). ...
Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey shows that there is still a lack of systematic understanding and organization of commonly used NLP techniques in RE. We believe one hurdle facing the industry is lack of shared knowledge of NLP techniques and their usage in RE tasks. In this paper, we present our effort to synthesize and organize 57 most frequently used NLP techniques in RE. We classify these NLP techniques in two ways: first, by their NLP tasks in typical pipelines and second, by their linguist analysis levels. We believe these two ways of classification are complementary, contributing to a better understanding of the NLP techniques in RE and such understanding is crucial to the development of better NLP tools for RE.
... User involvement is a key ingredient of requirements elicitation, which has been shown to contribute positively to system success [1]. In Crowd-Based Requirements Engineering (CrowdRE) [2], user involvement typically involves gathering and analyzing user feedback. Software companies may obtain such feedback in two ways [2]: (i) pull: by explicitly requesting the crowd to express their needs, often via an online platform such as a user forum [3], [4]; or (ii) push: by analyzing unsolicited inputs that are voluntarily provided by the users, such as reviews in app stores [5], [6]. ...
... In Crowd-Based Requirements Engineering (CrowdRE) [2], user involvement typically involves gathering and analyzing user feedback. Software companies may obtain such feedback in two ways [2]: (i) pull: by explicitly requesting the crowd to express their needs, often via an online platform such as a user forum [3], [4]; or (ii) push: by analyzing unsolicited inputs that are voluntarily provided by the users, such as reviews in app stores [5], [6]. ...
... Crowd-based requirements engineering (CrowdRE) refers to automated or semi-automatic ways of obtaining and analysing data from a crowd in order to develop validated user requirements [24,25]. In traditional RE methods, a small number of people are interviewed or gathered in focus groups. ...
Effective software requirement engineering plays a crucial role in bridging the gap between stakeholders and software products. The success of any software project heavily relies on accurately capturing, analysing, and documenting stakeholders' needs and expectations. This article provides a comprehensive review of various software requirement engineering techniques that facilitate the alignment of stakeholder requirements with software product development. Software requirements are extracted from a variety of stakeholders, but the decision of "what to develop" is a difficult one. Stakeholders' lack of clarity about what they want makes requirement elicitation a difficult and vital task. It explores the significance of understanding stakeholders' perspectives, discusses popular requirement engineering approaches, and highlights their strengths and limitations. The article concludes by emphasizing the importance of selecting appropriate requirement engineering techniques based on the project's context and offers recommendations for future research in this domain.
... With the rise of mobile applications and social media, research proposed to elicit feedback from crowds of geographically distributed users [19] and called for the mass participation of software users during different stages of software development [20]. Pagano and Maalej [21], and Hoon [22] were among the first to study user feedback in app stores. ...
In this paper, we identified marginalized communities' ethical concerns about social platforms. We performed this identification because recent platform malfeasance indicates that software teams prioritize shareholder concerns over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. We first scraped 586 marginalized communities' subreddits, aggregated a dataset of their social platform mentions and manually annotated mentions of ethical concerns in these data. We subsequently analyzed trends in the manually annotated data and tested the extent to which ethical concerns can be automatically classified by means of natural language processing (NLP). We found that marginalized communities' ethical concerns predominantly revolve around discrimination and misrepresentation, and reveal deficiencies in current software development practices. As such, researchers and developers could use our work to further investigate these concerns and rectify current software flaws.
... Reviews in app stores are a rich source of user feedback for crowd-based RE [10]. Pagano and Maalej [27] conducted an empirical study on user feedback in app stores, showing that app stores can serve as communication channels between users and developers, allowing to continuously receive bug reports, feature requests, praise, etc. Developers can use reviews to understand new user needs since they provide more insight than plain statistics into how apps are actually used. ...
[Context and motivation] Requirements assessment by means of the Kano model is common practice. As suggested by the original authors, these assessments are done by interviewing stakeholders and asking them about the level of satisfaction if a certain feature is well implemented and the level of dissatisfaction if a feature is not or not well implemented. [Question/problem] Assessments via interviews are time-consuming, expensive, and can only capture the opinion of a limited set of stakeholders. [Principal ideas/results] We investigate the possibility to extract Kano model factors (basic needs, performance factors, and delighters) from a large set of user feedback (i.e., app reviews). We implemented, trained, and tested several classifiers on a set of 2,592 reviews. In a 10-fold cross-validation, a BERT-based classifier performed best with an accuracy of 0.928. To assess the classifiers' generalization, we additionally tested them on another independent set of 1,622 app reviews. The accuracy of the best classifier dropped to 0.725. We also show that misclassifications correlate with human disagreement on the labels. [Contribution] Our approach is a lightweight and automated alternative for identifying Kano model factors from a large set of user feedback. The limited accuracy of the approach is an inherent problem of missing information about the context in app reviews compared to comprehensive interviews, which also makes it hard for humans to extract the factors correctly.
... Dieste and Juristo [14] and Ambreen [15] reported on the empirical studies that examine the productiveness of some of the above-mentioned elicitation approaches and frameworks. Different latest studies have considered several present-day challenges and latest possibilities in this field, even directing towards crowdbased requirement engineering [16] and also data driven requirement engineering [17][18]. ...
The discipline of Requirement Engineering is
associated with great number of problems, starting from the
elicitation phase to the implementation of these requirements
in the software industry. Although many approaches have been
proposed by the researchers, but very small number of
approaches are used in software industries. It is known that
requirements which are not well understood and unrestricted,
changes to the scope of requirements leads numerous software
projects to failure. So, good requirements are very important
for project success. Paper is to explore about state of
approaches regarding requirement gathering by closely
exploring current approaches used by the practitioners or
professionals. Aim of this paper is to focus on approaches used
in software industry for elicitation purpose and the challenges
faced during the process of elicitation. For this we carried out
survey, based upon interviewing 20 professionals from 10
different Pakistani Information Technology companies. We
analyzed these findings by qualitative methods. This includes
semi structure interviews for data gathering. Various results
came out from these findings. Group interaction approaches
together with (brainstorming and meetings) are considered as
most favorable approaches for elicitation purpose, except in
small projects. Lastly, we recognized some challenges related
to stakeholders. Challenges comprise of complication in
prioritizing and capturing the needs of the customers. These
findings need to be demonstrated in context of this research.
We concluded that findings of the survey professional’s
knowledge should be to the interest of software industry;
knowledge should be examined in professional context.
... Practitioners however still use a manual approach for analysing the feedback [7,19]. Unfortunately, analyzing app reviews manually to exploit the useful information is challenging due to their large number and the difficulty in extracting actionable information from short informal texts [7,20]. Popular apps like WhatsApp Messenger can receive more than 300,000 reviews per month [21]; moreover, the review content can vary from informative and helpful one to content conveying hate and spam [1,2]. ...
The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis.
... The subdiscipline of RE that deals with obtaining user feedback from large groups of current and potential users (i.e., a "crowd") is typically referred to as Crowd-based RE, or CrowdRE in short [20], [21]. CrowdRE encompasses such aspects as motivating the crowd to provide (more) user feedback, eliciting requirements from user feedback such as text-based and usage data, and validating the identified requirements with the crowd. ...
An overwhelming number of users access app repositories like App Store/Google Play and social media platforms like Twitter, where they provide feedback on digital experiences. This vast textual corpus comprising user feedback has the potential to unearth detailed insights regarding the users’ opinions on products and services. Various tools have been proposed that employ natural language processing (NLP) and traditional machine learning (ML) based models as an inexpensive mechanism to identify requirements in user feedback. However, they fall short on their classification accuracy over unseen data due to factors like the cost of generating voluminous de-biased labeled datasets and general inefficiency. Recently, Van Vliet et al. achieved state-of-the-art results extracting and classifying requirements from user reviews through traditional crowdsourcing. Based on their reference classification tasks and outcomes, we successfully developed and validated a deep-learning-backed artificial intelligence pipeline to achieve a state-of-the-art averaged classification accuracy of ~87% on standard tasks for user feedback analysis. This approach, which comprises a BERT-based sequence classifier, proved effective even in extremely low-volume dataset environments. Additionally, our approach drastically reduces the time and costs of evaluation, and improves on the accuracy measures achieved using traditional ML-/NLP-based techniques.
... The success of software can be seen from the percentage of achievement of the desired goal. One way to ensure the achievement of the desired goal is with Requirements Engineering (RE) [4]. RE is done by identifying the stakeholders involved and their needs, documenting in a form that can be analyzed, negotiated, and then implemented. ...
Users should always play a central role in the development of (software) solutions. The human-centered design (HCD) process in the ISO 9241-210 standard proposes a procedure for systematically involving users. However, due to its abstraction level, the HCD process provides little guidance for how it should be implemented in practice. In this chapter, we propose three concrete practical methods that enable the reader to develop usable security and privacy (USP) solutions using the HCD process. This chapter equips the reader with the procedural knowledge and recommendations to: (1) derive mental models with regard to security and privacy, (2) analyze USP needs and privacy-related requirements, and (3) collect user characteristics on privacy and structure them by user group profiles and into privacy personas. Together, these approaches help to design measures for a user-friendly implementation of security and privacy measures based on a firm understanding of the key stakeholders.
In an environment of steadily increasing competitiveness, data driven product development offers a great opportunity for automotive manufacturers. Vehicle usage data can be used, for example, to optimize technical components for their real-life stress or to develop new customer functions based on real user behavior. With the increasing interconnectedness of modern vehicles, usage data in the form of vehicle bus signals can be used for data-driven development. However, the use of this data often poses a challenge because it was designed for internal communication of the vehicle rather than for later analysis. Accordingly, this paper presents how data mining (DM) methods can be used to extract customer behavior from this data. The central idea of the presented methods is the derivation and enrichment of the vehicle bus signals with metadata of the vehicle usage, for example but not exclusively by applying statistical methods like machine learning. The method of metadata enrichment is embedded in an adapted version of the Cross Industry Standard Process for Data Mining (CRISP-DM). The developed methods are presented based on concrete application examples and finally a general recommendation for action for data-driven product development in the automotive sector is derivedKeywordsData driven developmentData miningConnected car data analytics
Requirements Engineering aims at supporting the understanding of the purpose of a software system to be built, and at keeping the whole design and development process aligned with it. Research in Requirements Engineering (RE) provides methods and techniques to support various activities in the requirements life cycle, from requirements elicitation to requirements verification and validation. Artificial Intelligence (AI) techniques are more and more exploited in such methods, including natural language processing techniques, since many RE artefacts are expressed as natural language text; techniques based on optimisation, machine learning, and deep learning with the objective of improving the efficiency of the analysts and decision-makers performing RE activities. In this chapter, we focus on two specific use cases in RE, namely, requirements elicitation from textual user feedback and requirements prioritisation. We present solutions to the two problems based on AI techniques, specifically machine learning, natural language processing, and Genetic Algorithms. The application of the proposed methods in industrial contexts allowed us to validate their usefulness in terms of increased efficiency of organisations during their decision-making processes. Finally, we discuss the use cases in the broader context of the RE management process, highlighting opportunities and limits of the AI approaches and current trends in the use of AI in RE.
In our current day and age, Earth suffers under the human ecological footprint, which influences our health and well-being. Technological solutions, including software-related ones, may help tackle these concerns for humanity. However, the development of such solutions requires special attention and effort to overcome human, public, and social barriers that might prevent them from being effective. The Requirements Engineering for Well-Being, Aging, and Health (REWBAH) workshop gathering in 2021 focused on addressing the challenge of how Requirements Engineering (RE) knowledge and practices can be applied to the development of information systems that support and promote long-lasting, sustained, and healthier behavior and choices by individuals. An interactive discussion among subject matter experts and practitioners participating in the REWBAH’21 revolved around several questions. In a subsequent qualitative analysis, the emerging themes were arranged in the sustainable-health RE (SusHeRE) framework to describe RE processes that address both sustainability and health goals. In this vision paper, we present our framework, which includes four main SusHeRE goals defined according to the changes in RE that we deem necessary for achieving a positive contribution of RE on sustainability and health. These goals involve improved RE Techniques, Multidisciplinary Expertise, Education Agenda, and Public and Social Ecology.
[Context/Motivation] In crowd requirements engineering, users are asked specific questions (explicit pull feedback) to elicit requirements. Existing approaches collect explicit pull feedback by asking the same questions to all users. [Problem] Not all questions are meaningful for all users, e.g. regarding a functionality they have not yet used. Furthermore, without knowing the user behaviour giving rise to the feedback, it is difficult to understand the reasons for the feedback. These reasons are important for deriving requirements. [Principal ideas] Our idea is to use the user behaviour (implicit feedback) to adapt the collection of explicit pull feedback and the derivation of requirements. We embed this collection of explicit pull feedback into a novel approach that makes use of a rich palette of discussion elements from crowd-based requirements engineering to motivate user participation and to support requirements derivation. [Contribution]. To our best knowledge, this is the first approach that combines the collection of implicit feedback and explicit feedback with discussion elements from crowd-based requirements engineering. We sketch our approach and our research and evaluation plan regarding the application of the approach in the context of the interdisciplinary and large-scale research project SMART-AGE with around 500 users.KeywordsRequirements engineeringCrowdUser feedbackImplicit feedback
[Context and motivation] Requirements assessment by means of the Kano model is common practice. As suggested by the original authors, these assessments are done by interviewing stakeholders and asking them about the level of satisfaction if a certain feature is well implemented and the level of dissatisfaction if a feature is not or not well implemented. [Question/problem] Assessments via interviews are time-consuming, expensive, and can only capture the opinion of a limited set of stakeholders. [Principal ideas/results] We investigate the possibility to extract Kano model factors (basic needs, performance factors, delighters, irrelevant) from a large set of user feedback (i.e., app reviews). We implemented, trained, and tested several classifiers on a set of 2,592 reviews. In a 10-fold cross-validation, a BERT-based classifier performed best with an accuracy of 92.8%. To assess the classifiers’ generalization, we additionally tested them on another independent set of 1,622 app reviews. The accuracy of the best classifier dropped to 72.5%. We also show that misclassifications correlate with human disagreement on the labels. [Contribution] Our approach is a lightweight and automated alternative for identifying Kano model factors from a large set of user feedback. The limited accuracy of the approach is an inherent problem of missing information about the context in app reviews compared to comprehensive interviews, which also makes it hard for humans to extract the factors correctly.KeywordsRequirements AnalysisKano ModelApp Store AnalyticsMachine LearningNLP
App reviews provide a rich source of feature-related information that can support requirement engineering activities. Analysing them manually to find this information, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for ‘feature-specific analysis’. Unfortunately, the effectiveness of these approaches has been evaluated using different methods and datasets. Replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present two empirical studies. In the first study, we evaluate opinion mining approaches; the approaches extract features discussed in app reviews and identify their associated sentiments. In the second study, we evaluate approaches searching for feature-related reviews. The approaches search for users’ feedback pertinent to a particular feature. The results of both studies show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.
Pandemic situations impact the ability of the startups to identify the product features that have match with market needs: the activity that requires direct interaction with the customers at the same physical space. Online tools can overcome this limitation, but early-stage startups have too limited resources and lack of access to the potential customers, which make their online interactions quite limited. The divergent creativity is required to identify the requirement elicitation methods and tools that could help startups to identify product/market fit with limited same physical space interaction with customers. The open innovation involving academia, experts, and researchers could help startups to get access to the market needs. This chapter reports one such consulting experience of the author with the Madrid (Spain)-based startup which successfully identified its market in pandemic time through global market research driven by secondary studies, primary research involving potential clients (or users) through online means, and limited interactions at the same physical space. Daily brainstorming with a team of researchers, experts, and professors helped to generate divergent ideas about identifying markets amid the pandemic and testing them in real context that proved to be successful for the startup. Overall impact is the ability of startup to innovate its business model for foreign markets.KeywordsStartupsStartup-academia partnershipsCoronavirusBusiness model innovationCreativityGlobal market research
Innovation is fueled by customer feedback. Market success is dependent on gathering client feedback and making company decisions based on it. The reason for this is because firms with newness and smallness liabilities had a limited understanding of their target market. This is due to the product being too original at first, with little market. Customer input is an important aspect of business model innovation since it helps to improve customer interactions. This chapter examines feedback management in the context of startups, with a special emphasis on pandemic practices. The reported data is based on an evaluation of insights from real feedback management techniques of numerous industries’ early-stage European startups throughout the outbreak. Some of the many feedback acquisition tools addressed in this chapter are Facebook, LinkedIn, Twitter, WhatsApp, emails, websites (made on Wix and WordPress), Slacks, Google Forms, Skype, and Zoom. The framework for implementing feedback collecting technology is offered with the goal of improving selection decisions that are made in response to it. The decision to include only these tools stems from the fact that throughout the epidemic, the startup community made great use of these technologies. Finally, the process for gathering and analyzing customer feedback and the underlying technology for feedback management greatly depend on the startup life cycle as well as whether the business is concentrating on domestic or international markets.
User feedback is an important resource in modern software development, often containing requirements that help address user concerns and desires for a software product. The feedback in online channels is a recent focus for software engineering researchers, with multiple studies proposing automatic analysis tools. In this work, we investigate the product forums of two large open source software projects. Through a quantitative analysis, we show that forum feedback is often manually linked to related issue tracker entries and product documentation. By linking feedback to their existing documentation, development teams enhance their understanding of known issues, and direct their users to known solutions. We discuss how the links between forum, issue tracker, and product documentation form a requirements ecosystem that has not been identified in the previous literature. We apply state-of-the-art deep-learning to automatically match forum posts with related issue tracker entries. Our approach identifies requirement matches with a mean average precision of 58.9% and hit ratio of 82.2%. Additionally, we apply deep-learning using an innovative clustering technique, achieving promising performance when matching forum posts to related product documentation. We discuss the possible applications of these automated techniques to support the flow of requirements between forum, issue tracker, and product documentation.
Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.
Pazarlama ile ilgili bir yazılım sistem kalitesini artırmak için, tüketici geribildirimleri sistem tasarımı sırasında değerlendirilmelidir. Bu araştırmanın amacı tüketicilerin kullanacağı iyi-olma yazılımları ile ilgili gereksinimlerini keşfetmektir. Tüketicilerin yazılım ile mutluluklarını nasıl artırabilecekleri ve ikna teknolojileri ile oyunlaştırma teknikleri dahil tüketicileri sistemde tutan ve mutluluklarını etkileyen detaylar irdelenmektedir. Çalışmada karma araştırma yöntemi uygulandı. Yapılan tematik analiz sonucunda, katılımcıların bahsettikleri bir pazarlama iyi-olma uygulamasının mutlu eden özelliklerinin yanı sıra, işlevsel özellikleri, interaktif özellikleri ve ürün-fiyat özellikleri ayrı temalarda gruplandırıldı.
Requirements engineering (RE) is gaining acceptance by industries and practitioners as a significant process in systems development. Similarly, the publications on the research related to the RE topic progresses. However, there is a lack of study on the progressions of this research based on a bibliographic portfolio. This paper presents the emergence of the RE field and mapping the state of the art of scientific production using a bibliometric survey approach. This study identifies the research trends based on the observations of scientific production over the past two decades. Quantitative and qualitative information in requirements engineering research through various bibliographic attributes are presented, such as the leading journals, top-cited articles, top authors, and top countries. Research trends in RE can be observed based on the number of research themes, and the involvement of various disciplines from RE begin to get acknowledged until this study was conducted. The results provide evidence and an overview of future directions in this research field.
Diversity is a great challenge for software engineers in the social sector context. The objective of this paper is to contribute to the identification of the RE processes and associated challenges in releasing the software in the social sector markets for which an exploratory case study is conducted. The outcome of the case study indicates that the diversity limits the ability to involve the representative samples of user populations using the same set of RE tools and techniques as one size fits all solution for all segments. The diverse user base must be partitioned into different segments, with each segment triggered using a suitable set of RE techniques i.e., traditional and crowd-based RE. The diverse perspectives learned as a result of the interaction with each segment, must be merged together into a single perspective about the software meant to be used in the social sector. There is a need for a new RE process specially designed for handling the complexities of the social sector, which this paper terms as Social Sector Requirement Engineering (SSRE). There is an increased need for collaboration between government social sector institutions and software engineers to get access to diverse customers to improve software quality.
Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user group. However, there are still many issues. Among others, a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are received. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by: 1) the labelled graph proposed as the representation of the candidate solutions during the iterative evolution; 2) the generalized entropy proposed and defined as the measurement of the solutions; 3) the genetic algorithm designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.
Users of the Twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis. Some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs. Therefore, tweets have the potential to aid in the continuous evolution of software applications. Despite the existence of such relevant tweets, little is known about their number and content. In this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques. Although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies. Furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically.
Open-Source Software (OSS) community members report bugs, request features or clarifications by writing messages (in unstructured natural language) to mailing lists. Analysts examine them dealing with an effort demanding and error prone task, which requires reading huge threads of emails. Automated support for retrieving relevant information and particularly for recognizing discussants’ intentions (e.g., suggesting, complaining) can support analysts, and allow them to increase the performance of this task. Online discussions are almost synchronous written conversations that can be analyzed applying computational linguistic techniques that build on the speech act theory. Our approach builds on this observation. We propose to analyze OSS mailing-list discussions in terms of the linguistic and non-linguistic acts expressed by the participants, and provide a tool-supported speech-act analysis method. In this paper we describe this method and discuss how to empirically evaluate it. We discuss the results of the first execution of an empirical study that involved 20 subjects.
Users' feedback is vital to improve software quality and it provides developers with a rich knowledge on how software meets users' requirements in practice. Feedback informs how software should adapt, or be adapted, at runtime and what evolutionary actions to take in the next release. However, studies have noted that accommodating the different preferences of users on how feedback should be requested is a complex task and requires a careful engineering process. This calls for an adaptive feedback acquisition mechanisms to cater for such variability. In this paper, we tackle this problem by employing the concept of Persona to aid software engineers understand the various users' behaviours and improve their ability to design feedback acquisition techniques more efficiently. We create a set of personas based on a mixture of qualitative and quantitative studies and propose PAFA, a Persona-based method for Adaptive Feedback Acquisition.
App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).
Social networks have changed our daily life and they have the potential to significantly influence and support Requirements Engineering (RE) activities. Social network-based RE approaches will allow us to overcome limitations of traditional approaches and allow end users to play a more prominent role in RE. They are key stakeholders in many software projects. However, involving end users is challenging, particularly when they are not within organizational reach. The goal of our work is to increase end user involvement in RE. In this paper we present an approach where we harness a social network to perform RE activities such as elicitation, prioritization and negotiation. Our approach was applied in three studies where students used Facebook to actively participate in RE activities of different projects. Although there are limitations, the results show that a popular social network site can support distributed RE.
Crowdsourcing is an emerging business model where tasks are accomplished by the general public; the crowd. Crowdsourcing has been used in a variety of disciplines, including information systems development, marketing and operationalization. It has been shown to be a successful model in recommendation systems, multimedia design and evaluation, database design, and search engine evaluation. Despite the increasing academic and industrial interest in crowdsourcing, there is still a high degree of diversity in the interpretation and the application of the concept. This paper analyses the literature and deduces a taxonomy of crowdsourcing. The taxonomy is meant to represent the different configurations of crowdsourcing in its main four pillars: the crowdsourcer, the crowd, the crowdsourced task and the crowdsourcing platform. Our outcome will help researchers and developers as a reference model to concretely and precisely state their particular interpretation and configuration of crowdsourcing.
Adaptive systems are characterized by the ability to monitor changes in their volatile world and react to monitored changes when needed. The ultimate goal of adaptation is that users' requirements are always met correctly and efficiently. Adaptation is traditionally driven by the changing state of the system internal and its surrounding environment. Such state should be monitored and analyzed to decide upon a suitable alternative behaviour to adopt. In this paper, we introduce another driver for adaptation which is the users' collective judgement on the alternative behaviors of a system. This judgmenet should be infered from the individual users' feedback given iteratviely during the lifetime of a system. Users' feedback reflects their main interest which is the validity and the quality of a system behaviour as a means to meet their requirements. We propose social adaptation which is a specific kind of adaptation that treats users' feedback, obtained during the software lifetime, as a primary driver in planning and guiding adaptation. We propose a novel requirements engineering modelling and analysis approach meant for systems adopting social adaptation. We evaluate our approach by applying it in practice and report on the results.
Post-deployment user feedback such as feature requests and bug reports have the potential to improve software quality and reveal missing functionality. Nevertheless, in practice software companies often ignore user feedback and simply stick with predefined roadmaps and development plans, because of two main problems. First, in order to benefit from user feedback, developers have to analyze, consolidate, and structure it, which becomes time-consuming when feedback volume is high. Second, in order to prioritize their work, developers need to assess how many users are affected by a particular feedback, by identifying duplicate and similar feedback in a manual way. This thesis describes Portneuf, a framework which tackles both problems. It consolidates user feedback by employing a context-aware, hybrid recommender system. Moreover, it introduces FeedbackRank, an algorithm which allows developers to assess the importance of collected user feedback to the user community. We demonstrate the feasibility and applicability of our approach in two real-world applications. In a quasi-experiment with a large number of users, we showed that Portneuf increases developers' efficiency when working with user feedback. The framework significantly reduces the amount of user feedback duplicates by over 67%, with the recommender system showing a hit-rate of over 82%. Moreover, we obtained an average precision of around 76% for FeedbackRank, which suggests that it provides a valuable estimation of what is important to the user community.
Crowdsourcing is an emerging and promising approach which involves delegating a variety of tasks to an unknown workforce—the crowd. Crowdsourcing has been applied quite successfully in various contexts from basic tasks on Amazon Mechanical Turk to solving complex industry problems, e.g. InnoCentive. Companies are increasingly using crowdsourcing to accomplish specific software development tasks. However, very little research exists on this specific topic. This paper presents an in-depth industry case study of crowdsourcing software development at a multinational corporation. Our case study highlights a number of challenges that arise when crowdsourcing software development. For example, the crowdsourcing development process is essentially a waterfall model and this must eventually be integrated with the agile approach used by the company. Crowdsourcing works better for specific software development tasks that are less complex and stand-alone without interdependencies. The development cost was much greater than originally expected, overhead in terms of company effort to prepare specifications and answer crowdsourcing community queries was much greater, and the time-scale to complete contests, review submissions and resolve quality issues was significant. Finally, quality issues were pushed later in the lifecycle given the lengthy process necessary to identify and resolve quality issues. Given the emphasis in software engineering on
identifying bugs as early as possible, this is quite problematic.
MyERP is a fictional developer of an Enterprise Resource Planning (ERP) system. Driven by the competition, they face the challenge of losing market share if they fail to de-ploy a Software as a Service (SaaS) ERP system to the European market quickly, but with high quality product. This also means that the requirements engineering (RE) activities will have to be performed efficiently and provide solid results. An additional problem they face is that their (potential) stakeholders are phys-ically distributed, it makes sense to consider them a "crowd". This competition paper suggests a Crowd-based RE approach that first identifies the crowd, then collects and analyzes their feedback to derive wishes and needs, and validate the results through prototyping. For this, techniques are introduced that have so far been rarely employed within RE, but more "traditional" RE techniques, will also be integrated and/or adapted to attain the best possible result in the case of MyERP.
Online user feedback is principally used as an information source for evaluating customers' satisfaction for a given goods, service or software application. The increasing attitude of people towards sharing comments through the social media is making online user feedback a resource containing different types of valuable information. The huge amount of available user feedback has drawn the attention of researchers from different fields. For instance, data mining techniques have been developed to enable information extraction for different purposes, or the use of social techniques for involving users in the innovation of services and processes. Specifically, current research and technological efforts are put into the definition of platforms to gather and/or analyze multi-modal feedback. But we believe that the understanding of the type of concepts instantiated as information contained in user feedback would be beneficial to define new methods for its better exploitation. In our research, we focus on online explicit user feedback that can be considered as a powerful means for user-driven evolution of software services and applications. Up to our knowledge, a conceptualization of user feedback is still missing. With the purpose of contributing to fill up this gap we propose an ontology, for explicit online user feedback that is founded on a foundational ontology and has been proposed to describe artifacts and processes in software engineering. Our contribution in this paper concerns a novel user feedback ontology founded on a Unified Foundational Ontology (UFO) that supports the description of analysis processes of user feedback in software engineering. We describe the ontology together with an evaluation of its quality, and discuss some application scenarios.
MyERP is a fictional developer of an Enterprise Resource Planning (ERP) system. Driven by the competition, they face the challenge of losing market share if they fail to deploy a Software as a Service (SaaS) ERP system to the European market quickly, but with high quality product. This also means that the requirements engineering (RE) activities will have to be performed efficiently and provide solid results. An additional problem they face is that their (potential) stakeholders are physically distributed, it makes sense to consider them a “crowd”. This competition paper suggests a Crowd-based RE approach that first identifies the crowd, then collects and analyzes their feedback to derive wishes and needs, and validate the results through prototyping. For this, techniques are introduced that have so far been rarely employed within RE, but more “traditional” RE techniques, will also be integrated and/or adapted to attain the best possible result in the case of MyERP.
Stakeholders who are highly distributed form a large, heterogeneous online group, the so-called “crowd”. The rise of mobile, social and cloud apps has led to a stark increase in crowd-based settings. Traditional requirements engineering (RE) techniques face scalability issues and require the co-presence of stakeholders and engineers, which cannot be realized in a crowd setting. While different approaches have recently been introduced to partially automate RE in this context, a multi-method approach to (semi-)automate all RE activities is still needed. We propose “Crowd-based Requirements Engineering” as an approach that integrates existing elicitation and analysis techniques and fills existing gaps by introducing new concepts. It collects feedback through direct interactions and social collaboration, and by deploying mining techniques. This paper describes the initial state of the art of our approach, and previews our plans for further research.
Mobile platforms and applications are an exciting and important phenomenon in today's software and business world. They are being woven into the fabric of daily life faster than expected. Continuous collection of user feedback enabling the improvement of platforms and applications becomes critical to support the continuous evolution of mobile systems. Particularly user feedback is needed to provide systems that best fit user needs. We have designed a mobile feedback approach, which enables users to document individual feedback on mobile systems in situ. This information can then be evaluated and used as new requirements by developers. Based on this solution we have developed a feedback app for two different mobile platforms. Furthermore, we have conducted a study with smartphone users applying this approach and communicating feedback on a mobile platform and pre-installed apps. The study revealed that users were able to give individual feedback and that a large amount of this feedback was considered to be useful for mobile system improvement by a platform developer.
The World Wide Web and the services it provides are continually evolving. Even for a single time instant, it is a complex task to methodologically determine the infrastructure over which these services are provided and the corresponding effect on user perceived performance. For such tasks, researchers typically rely on active measurements or large numbers of volunteer users. In this paper, we consider an alternative approach, which we refer to as passive crowd-based monitoring. More specifically, we use passively collected proxy logs from a global enterprise to observe differences in the quality of service (QoS) experienced by users on different continents. We also show how this technique can measure properties of the underlying infrastructures of different Web content providers. While some of these properties have been observed using active measurements, we are the first to show that many of these properties (such as location of servers) can be obtained using passive measurements of actual user activity. Passive crowd-based monitoring has the advantages that it does not add any overhead on Web infrastructure, it does not require any specific software on the clients, but still captures the performance and infrastructure observed by actual Web usage.
This review article discusses various techniques of environmental monitoring (EM) systems and what is required for the variations in hardware implementation and/or algorithmic logic. This review presents an overview of the existing state-of-the-art practices of environmental monitoring systems and is mainly focused on energy-efficient and low-cost environment monitoring systems. The following are some of the major factors that usually rule the development of EM systems, namely, energy efficiency, cost of the overall system, response time of the sensor module, good accuracy of the system, adequate signal-to-noise ratio, radio frequency interference/electromagnetic interference (RFI/EMI) rejection during varying atmospheric conditions and in inhomogeneous environments, a user friendly interface with the computer, and complexity of computation. The above concerns are also recognized by reference to research articles on environmental monitoring systems. Emphasis is on the necessity of robust systems that address all or most of the above mentioned criteria.
An increasing part of software development is devoted to products that are offered to an open market with many customers. Market-driven development imposes special challenges for the requirements engineering process. This chapter provides an overview of the special characteristics of market-driven requirements engineering and describes the most important challenges of the area. Key elements of market-driven requirements engineering processes are presented together with a definition of process quality. Requirements state models and requirements repositories are also described and examples of typical solutions to progress tracking and data management are provided. The difficult problem of release planning is also discussed and an industrial example of a release planning process is given.
Current requirements engineering practices for gathering user input are characterized by a number of communication gaps between users and engineers, which might lead to wrong requirements. The problem situations and context which underlie user input are either gathered back in time, or submitted with wrong a level of details. We think that making user input a first order concern of both software processes and software systems harbours many innovation opportunities. We propose and discuss a continuous and context-aware approach for communicating user input to engineering teams and other users, by a) instrumenting the problem domain, b) proactively recommending to share feedback and c) annotating graphical interfaces.
In market-driven software development there is a strong need for support to handle congestion in the requirements engineering
process, which may occur as the demand for short time-to-market is combined with a rapid arrival of new requirements from
many different sources. Automated analysis of the continuous flow of incoming requirements provides an opportunity to increase
the efficiency of the requirements engineering process. This paper presents empirical evaluations of the benefit of automated
similarity analysis of textual requirements, where existing information retrieval techniques are used to statistically measure
requirements similarity. The results show that automated analysis of similarity among textual requirements is a promising
technique that may provide effective support in identifying relationships between requirements.
A Novel Method for Large Scale Requirement Elicitation
- V Dheepa
- D J Aravindhar
- C Vijayalakshmi
V. Dheepa, D.J. Aravindhar, and C. Vijayalakshmi, "A Novel Method for Large Scale Requirement Elicitation,"
Int'l J. Eng. and Innovative Technology, vol. 2, no. 7, 2013, pp. 375-379.
A Novel Method for Large Scale Requirement Elicitation
- dheepa