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Portfolio Evolution Analysis for SPL Scoping: Unveiling the dynamics with dedicated time-series dashboards
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In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the “intelligence” stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely “design” and “choice”? To further explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess whether and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges that decision-focused visualization tools face in realizing their full potential to support all stages of the decision making process. It reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Based on our findings, to better support the choice stage, future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility levels. Our curated list of the 88 surveyed visualization tools is available in the OSF link
(https://osf.io/nrasz/?view_only=b92a90a34ae241449b5f2cd33383bfcb)
.
Context
Variant-Rich Systems (VRSs), such as Software Product Lines or variants created through clone & own, are created to satisfy different needs while reusing existing assets. The long lifespan of families of variants, and the scale of both the technical side (implementation size) and the organizational side (roles diversity) make their maintenance and evolution a challenge. Visualization tools are a needed companion.
Objective
We aim at mapping the current state of visualization interventions in the area of VRS evolution. We tackle evolution in both the functionality and the variability management architecture. Three research questions are posed: What sort of analysis is being conducted? (Analysis perspective); What sort of visualizations are displayed? (Visualization perspective); What types of research have been reported and how have they been evaluated? (Maturity perspective).
Methods
We performed a systematic mapping study including automated search in digital libraries, expert knowledge, and snowballing.
Results
The study reports on 41 visualization approaches to cope with VRS evolution. Analysis wise, feature identification and location is the most popular scenario, followed by variant integration towards a Software Product Line. As for visualization, nodelink diagram visualization is predominant while researchers have come up with a wealth of ingenious visualization approaches. Finally, maturity wise, almost half of the studies are solution proposals. Most of the studies provide proof-of-concepts, some of them also include public available tools, yet very few face proof-of-value.
Conclusions
This systematic mapping study introduces a comparison framework where to frame future studies. It also points out distinct research gaps worth investigating as well as shortcomings in the evidence about relevance and contextual considerations (e.g., scalability).
This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in “dashboard genres” such as
narrative
,
analytical
, or
embedded dashboard
. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online:
https://dashboarddesignpatterns.github.io</uri
In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data.
Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both methodologies promise similar benefits, they are based on different foundations. SPL encourages systematic reuse that exploits the commonalities of various products belonging to a common domain and manages their variations systematically. In contrast, ASD stresses a flexible and rapid development of products using iterative and incremental approaches. ASD encourages active involvement of customers and their frequent feedback. Both ASD and SPL require alternatives to extend agile methods for several reasons such as (1) to manage reusability and variability across the products of any domain, (2) to avoid the risk of developing core assets that will become obsolete and not used in future projects, and (3) to meet the requirements of changing markets. This motivates the researchers for the integration of ASD and SPL approaches. As a result, an innovative approach called agile product line engineering (APLE) by integrating SPL and ASD has been introduced. The principal aim of APLE is to maximize the benefits of ASD and SPL and address the shortcomings of both. However, combining both is a major challenge. Researchers have proposed a few approaches that try to put APLE into practice, but none of the existing approaches cover all APLE features needed. This paper proposes a new dynamic variability approach for APLE that uses APLE practices for reusing features. The proposed approach (PA) is based on the agile method Scrum and the reactive approach of SPL. In this approach, reusable core assets respond reactively to customer requirements. The PA constructs and develops the SPL architecture iteratively and incrementally. It provides the benefits of reusability and maintainability of SPLs while keeping the delivery-focused approach from agile methods. We conducted a quantitative survey of software companies applying the APLE to assess the performance of the PA and hypotheses of empirical study. Findings of empirical evaluation provide evidence on integrating ASD and SPL and the application of APLE into practices.
Product Line Engineering (PLE) is a crucial practice in many software development environments where software systems are complex and developed for multiple customers with varying needs. At the same time, many development processes are use case-driven and this strongly influences their requirements engineering and system testing practices. In this paper, we propose, apply, and assess an automated system test case classification and prioritization approach specifically targeting system testing in the context of use case-driven development of product families. Our approach provides: (i) automated support to classify, for a new product in a product family, relevant and valid system test cases associated with previous products, and (ii) automated prioritization of system test cases using multiple risk factors such as fault-proneness of requirements and requirements volatility in a product family. Our evaluation was performed in the context of an industrial product family in the automotive domain. Results provide empirical evidence that we propose a practical and beneficial way to classify and prioritize system test cases for industrial product lines.
The analysis of software product lines is challenging due to the potentially large number of products, which grow exponentially in terms of the number of features. Product sampling is a technique used to avoid exhaustive testing, which is often infeasible. In this paper, we propose a classification for product sampling techniques and classify the existing literature accordingly. We distinguish the important characteristics of such approaches based on the information used for sampling, the kind of algorithm, and the achieved coverage criteria. Furthermore, we give an overview on existing tools and evaluations of product sampling techniques. We share our insights on the state-of-the-art of product sampling and discuss potential future work.
Many existing approaches have exploited the similarity notion to analyze software systems. In product-line engineering, similarity notion has been considered to fulfill analysis objectives, such as improving the testing effectiveness and reducing the testing efforts. However, most of the existing approaches consider in the similarity measurement only information of high level of abstraction, such as the feature selections of variants. In this paper, we present the notion of similarity in product-line engineering using different types of problem-space as well as solution-space information. In particular, we discuss different scenarios of measuring the similarity between variants and the possibility of combining different types of information to output the similarity between the compared variants. Moreover, we realized these scenarios in the industrial variant management tool pure::variants to fulfill analysis functionalities.
[Context] Controlled experiments are an important empirical method to generate and validate theories. Many software engineering experiments are conducted with students. It is often claimed that the use of students as participants in experiments comes at the cost of low external validity while using professionals does not. [Objective] We believe a deeper understanding is needed on the external validity of software engineering experiments conducted with students or with professionals. We aim to gain insight about the pros and cons of using students and professionals in experiments. [Method] We performed an unconventional, focus group approach and a follow-up survey. First, during a session at ISERN 2014, 65 empirical researchers argued and discussed the use of students in experiments with an open mind. Afterwards, we revisited the topic and elicited experts’ opinions to foster discussions. Then we derived 14 statements and asked the 58 researchers to provide their level of agreement with the statements. Finally, we analyzed the researchers’ opinions and used the findings to further discuss the statements. [Results] Our survey results showed that, in general, the respondents disagreed with us about the drawbacks of professionals. We, on the contrary, strongly believe that no population (students, professionals, or others) can be deemed better than another in absolute terms. [Conclusion] Using students as participants remains a valid simplification of reality needed in laboratory contexts. It is an effective way to advance software engineering theories and technologies but, like any other aspect of study settings, should be carefully considered during the design, execution, interpretation, and reporting of an experiment. The key is to understand which developer population portion is being represented by the participants in an experiment. Thus, a proposal for describing experimental participants is put forward.
Software Product Lines (SPLs) are families of systems that share common assets allowing a disciplined reuse. Rarely SPLs start from scratch, instead they usually start from a set of existing systems that undergo a reengineering process. Many approaches to conduct the reengineering process have been proposed and documented in research literature. This scenario is a clear testament to the interest in this research area. We conducted a systematic mapping study to provide an overview of the current research on reengineering of existing systems to SPLs, identify the community activity in regarding of venues and frequency of publications in this field, and point out trends and open issues that could serve as references for future research. This study identified 119 relevant publications. These primary sources were classified in six different dimensions related to reengineering phases, strategies applied, types of systems used in the evaluation, input artefacts, output artefacts, and tool support. The analysis of the results points out the existence of a consolidate community on this topic and a wide range of strategies to deal with different phases and tasks of the reengineering process, besides the availability of some tools. We identify some open issues and areas for future research such as the implementation of automation and tool support, the use of different sources of information, need for improvements in the feature management, the definition of ways to combine different strategies and methods, lack of sophisticated refactoring, need for new metrics and measures and more robust empirical evaluation. Reengineering of existing systems into SPLs is an active research topic with real benefits in practice. This mapping study motivates new research in this field as well as the adoption of systematic reuse in software companies.
A software product line comprises a family of software products that share a common set of features. Testing an entire product-line product-by-product is infeasible due to the potentially exponential number of products in the number of features. Accordingly, several sampling approaches have been proposed to select a presumably minimal, yet sufficient number of products to be tested. Since the time budget for testing is limited or even a priori unknown, the order in which products are tested is crucial for effective product-line testing. Prioritizing products is required to increase the probability of detecting faults faster. In this article, we propose similarity-based prioritization, which can be efficiently applied on product samples. In our approach, we incrementally select the most diverse product in terms of features to be tested next in order to increase feature interaction coverage as fast as possible during product-by-product testing. We evaluate the gain in the effectiveness of similarity-based prioritization on three product lines with real faults. Furthermore, we compare similarity-based prioritization to random orders, an interaction-based approach, and the default orders produced by existing sampling algorithms considering feature models of various sizes. The results show that our approach potentially increases effectiveness in terms of fault detection ratio concerning faults within real-world product-line implementations as well as synthetically seeded faults. Moreover, we show that the default orders of recent sampling algorithms already show promising results, which, however, can still be improved in many cases using similarity-based prioritization.
Software Product Line Engineering is a mature approach enabling the derivation of product variants by assembling reusable assets. In this context, domain experts widely use Feature Models as the most accepted formalism for capturing commonality and variability in terms of features. Feature Models also describe the constraints in feature combinations. In industrial settings, domain experts often deal with Software Product Lines with high numbers of features and constraints. Furthermore, the set of features are often regrouped in different subsets that are overseen by different stakeholders in the process. Consequently, the management of the complexity of large Feature Models becomes challenging. In this paper we propose a dedicated interactive visualisation paradigm to help domain experts and stakeholders to manage the challenges in maintaining the constraints among features. We build Feature Relations Graphs (Frogs) by mining existing product configurations. For each feature, we are able to display a Frog which shows the impact, in terms of constraints, of the considered feature on all the other features. The objective is to help domain experts to 1) obtain a better understanding of feature constraints, 2) potentially refine the existing feature model by uncovering and formalizing missing constraints and 3) serve as a recommendation system, during the configuration of a new product, based on the tendencies found in existing configurations. The paper illustrates the visualisation paradigm with the industrial case study of Renault's Electric Parking System Software Product Line.
A software-intensive company generally grows from one of its remarkable software products. Different approaches adopted by a company will contribute to its future evolution. The approach employed by FISCAN, a leading manufacturer of security inspection system in China, is software product line engineering (SPLE). This paper reviews the initiation and institutionalization of SPLE by FISCAN. This is a process started with a bottom-up approach from application engineering to domain engineering, and realized by a top-down practice from domain engineering back to application engineering. Eventually, a closed loop which connects domain engineering and application engineering is ready to accelerate core asset development and product development continuously. This paper offers a roadmap for the initiation and institutionalization of SPLE as it was developed at FISCAN, including establishing a core team of product line champions, formalizing matrix product line model (MPLM), developing core asset library and product lines and creating compatible process model.
Design research (DR) positions information technology artifacts at the core of the Information Systems discipline. However, dominant DR thinking takes a technological view of the IT artifact, paying scant attention to its shaping by the organizational context. Consequently, existing DR methods focus on building the artifact and relegate evaluation to a subsequent and separate phase. They value technological rigor at the cost of organizational relevance, and fail to recognize that the artifact emerges from interaction with the organizational context even when its initial design is guided by the researchers' intent. We propose action design research (ADR) as a new DR method to address this problem. ADR reflects the premise that IT artifacts are ensembles shaped by the organizational context during development and use. The method conceptualizes the research process as containing the inseparable and inherently interwoven activities of building the IT artifact, intervening in the organization, and evaluating it concurrently. The essay describes the stages of ADR and associated principles that encapsulate its underlying beliefs and values. We illustrate ADR through a case of competence management at Volvo IT.
A significant amount of research project funding is spent creating customized annotation systems, re-inventing the wheel once and again, developing the same common features. In this paper, we present WACline, a Software Product Line to facilitate customization of browser extension Web annotation clients. WACline reduces the development effort by reusing common features (e.g., highlighting and commenting) while putting the main focus on customization. To this end, WACline provides already implemented 111 features that can be extended with new ones. In this way, researchers can reduce the development and maintenance costs of annotation clients.
Software product line (SPL) scoping aids companies to define the boundaries of their resources such as products, domains, and assets, the target of reuse tasks scoping technical and organizational aspects. As scoping guides the management of the resources in SPL development, it becomes one of the core activities in this process. We can find in the literature several approaches on this topic, proposing techniques and methodologies to be applicable in different organizational scenarios. However, no work comprehensively reviews such approaches and describes the advances in state of the art in the last years. In this context, we look into identifying, analyzing, and extracting detailed characteristics from SPL scoping proposals found in the literature. These characteristics allowed us to compare these approaches, reason about their applicability, and identify existing limitations and research opportunities. Thus, we conducted a systematic literature review alongside snowballing, following a well-defined protocol to retrieve, classify and extract information from the literature. We analyzed a total of 58 studies, identifying 41 different approaches in the field, highlighting their similarities and differences, and establishing a generic scoping process. Furthermore, we discuss research opportunities in the SPL scoping field.
Comparison is one of the essential tasks that motivate a data set exploration, and it is particularly arduous when it involves large data sets. The objective of information visualization is to help gain insight into the data, and in this context, it should assist the user in the task of comparison. However, few visualization techniques have been specifically developed to support the comparison process, even less for large data sets. In this paper, we present a visualization technique, with its corresponding set of interactions, designed to assist in time-based large data sets comparison. The proposed solution is based on Dynamic Time Warping and presents a visual comparison of the misalignment between a set of time series. The proposal includes an overview of the misalignment between the data and a by-demand detailed view focusing on the comparison between any two of them. As a validation of the technique, we also introduce a case study on the historical meteorological data from Bahía Blanca city.
We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013–2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.
The paper describes a demonstration of pure::variants, a commercial tool for variant and variability management for product lines. The demonstration shows how flexible product line (PL) architectures can be built, tested and maintained by using the modeling and integration capabilities provided by pure::variants. With pure::variants being available for a long time, the demonstration (and the paper) combines both basics of pure::variants, known to parts of the audience, and new capabilities, introduced within the last year.
Product lines are designed to support the reuse of features across multiple products. Features are product functional requirements that are important to stakeholders. In this context, feature models are used to establish a reuse platform and allow the configuration of multiple products through the interactive selection of a valid combination of features. Although there are many specialized configurator tools that aim to provide configuration support, they only assure that all dependencies from selected features are automatically satisfied. However, no support is provided to help decision makers focus on likely relevant configuration options. Consequently, since decision makers are often unsure about their needs, the configuration of large feature models becomes challenging. To improve the efficiency and quality of the product configuration process, we propose a new approach that provides users with a limited set of permitted, necessary and relevant choices. To this end, we adapt six state-of-the-art recommender algorithms to the product line configuration context. We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations. The results of our evaluation show that recommender algorithms, such as CF-shrinkage, CF-significance weighting, and BRISMF, when applied in the context of product-line configuration can efficiently support decision makers in a most efficient selection of features.
Software Product Line (SPL) development is a new approach to software engineering which aims at the development of a whole range of products. However, as long as SPL can be useful, there are many challenges regarding the use of that approach. One of the main problems which hinders the adoption of Software Product Line (SPL) is the complexity regarding product management. In that context, we can remark the scoping problem. One of the existent ways to deal with scoping is the Product Portfolio Scoping (PPS). PPS aims to define the products that should be developed as well as their key features. In general, that approach is driven by marketing aspects, like cost of the product and customer satisfaction. Defining a product portfolio by using the many different available aspects is a NP-hard problem. This work presents an improved hybrid approach to solve the feature model selection problem, aiming at supporting product portfolio scoping. The proposal is based in a hybrid approach not dependent on any particular algorithm/technology. We have evaluated the usefulness and scalability of our approach using one real SPL (ArgoUML-SPL) and synthetic SPLs. As per the evaluation results, our approach is both useful from a practitioners perspective and scalable.
A Software Product Line (SPL) aims to support the development of a family of similar software products from a common set of shared assets. SPLs represent a long-term investment and have a considerable life-span. In order to realize a return-on-investment, companies dealing with SPLs often plan their product portfolios and software engineering activities strategically over many months or years ahead. Compared to single system engineering, SPL evolution exhibits higher complexity due to the variability and the interdependencies between products. This chapter provides an overview on concepts and challenges in SPL evolution and summarizes the state of the art. For this we first describe the general process for SPL evolution and general modeling concepts to specify SPL evolution. On this base, we provide an overview on the state-of-the-art in each of the main process tasks which are migration towards SPLs, analysis of (existing) SPL evolution, planning of future SPL evolution, and implementation of SPL evolution.
Automotive manufacturing ranks among the most extreme instances of systems and software product line engineering (PLE). The product family numbers in the millions, each product is highly complex in its own right, and the variation across products is literally astronomical in scale. This paper explores the aspects that make the domain extreme and the very specific implications they have for PLE. These implications include the need for efficient manufacturing, complexity management, concurrent development streams, globally distributed engineering and production, a hierarchical product family tree, multi-level variation binding, constraint management, and a highly robust and integrated PLE tooling environment. Happily, the PLE paradigm supporting these implications brings about a number of opportunities for analysis and automation that provide efficiencies of production previously unattainable. We focus on one example in depth: The management and automated generation of the many thousands of calibration parameters that determine vehicle-specific software behavior. Throughout, we use the vehicle product line at General Motors, which we believe to be the world's largest, to illustrate and ground our journey through automotive PLE.
Software product line development is a rather new topic area within domain-specific software engineering that builds on previous work in domain engineering. A crucial step in developing a product line is the scoping step, which seeks to determine the boundaries of the product line. This is one of the core planning activities that may determine the success or failure of the whole product line effort. In this paper, we seek to analyze the existing body of knowledge on product line scoping. As the relationship to domain engineering is very close, we also include domain scoping approaches in our analysis. Further, we look at activities related to product line scoping in fields of study other than software engineering. As a result of this survey we provide a taxonomy of existing approaches on both a problem level and a solution level. We also discuss the relative advantages of the various approaches, and show some ways of using our results for enhancing existing scoping approaches and developing new ones.
As a Software Product Line (SPL) evolves with increasing number of variant features and feature values, the feature correlations become extremely intricate. However, these correlations are often incompletely documented (e.g., In feature models) so that most features can only be configured manually. In order to make product configuration processes more efficient, we present an approach to extracting complex feature correlations from existing product configurations using association mining techniques. Then these correlations are pruned and prioritized in order to minimize the effort of correlation validation. Our approach is conducted on an industrial SPL with 100 product configurations across 480 features. While 80 out of the 100 configurations are used as training data to automatically extract 4834 complex feature correlations, the rest 20 configurations are used as test data to evaluate the improvement potential of configuration efficiency. In the end, avg. 25% features in each of the 20 products can be configured automatically.
In this paper we propose a mathematical program able to optimize the product portfolio scope of a software product line and sketch both a development and a release planning. Our model is based on the description of customer needs in terms of goals. We show that this model can be instantiated in several contexts such as a market customization strategy or a mass-customization strategy. It can deal with Software Product Line development from scratch as well as starting from a legacy software base. We demonstrate its applicability with an example based on a case study.
Software Product Lines (SPL) adoption can affect several aspects of an organization and it involves significant investment and risk. This way, SPL risk management is a crucial activity of SPL adoption. This study aims to identify SPL risks during the scoping and requirement disciplines to provide information to better understand risk management in SPL. In order to achieve the previous stated goal, a case study research was applied in an industrial project in the medical information management domain. Using the captured risks, a classification scheme was built and risk mitigation strategies were identified. We spent five months, totaling 79 hours, performing risk management (RM) in the scoping discipline and twelve months, totaling 148 hours, performing RM on the requirements discipline. We identified 32 risks during the scoping discipline and 20 risks during the requirements discipline, 14 risks occurred in both disciplines. Some identified risks are not particular to SPL development, however, they have their impact increased due to the SPL characteristic. All the study results and lessons learned are useful for all project managers and researchers who are considering the introduction of SPL risk management in industry or academia.
Successful software systems continuously evolve to accommodate ever-changing needs of customers. Accommodating the feature requests of all the customers in a single product increases the risks and costs of software maintenance. A possible approach to mitigate these risks is to transition the evolving software system (ESS) from a single system to a portfolio of related product variants, each addressing a specific customers’ segment. This evolution should be conducted such that the extent of modifications required in ESS's structure is reduced. The proposed method COPE+ uses preferences of customers on product features to generate multiple product portfolios each containing one product variant per segment of customers. Recommendations are given to the decision maker to update the product portfolios based on structural analysis of ESS. Product portfolios are compared with the ESS using statechart representations to identify the level of similarity in their behaviors. A proof of concept is presented by application to an open-source text editing system. Structural and behavioral analysis of candidate portfolios helped the decision maker to select one portfolio out of three candidates.
[Context and motivation]PLEvo-Scoping is a method intended to help Product Line (PL) scoping teams anticipate emergent features and distinguish unstable
from stable features, with the aim of preparing their PL for likely future adaptation needs. [Question/problem]This paper describes a quasi-experiment performed to characterize PLEvo-Scoping in terms of adequacy and feasibility. [Principal ideas/results] This quasi-experiment was performed by two scoping teams in charge of scoping the same PL, where one scoping team applied
first an existing PL scoping approach and then PLEvo-Scoping, while the other scoping team interweaved activities from both.
The two approaches achieved similar results: The method could be applied in just one day, and it was considered adequate and
feasible. [Contribution] Ideas on how to improve the method and its tool support have been obtained, and similar results are expected from other professionals
facing the problem of evolution-centered PL scoping. However, further empirical studies should be performed.
Software Product Lines are a mean to improve the economic performance of firms that offer several products to a market by systematically reusing software artifacts. In most cases the definitive company goal is profit maximization. That can be reached by increasing revenue or by reducing cost. Revenue is increased by offering products with a wide variety of features to an audience willing to pay. However, the fewer features are realized the fewer cost incur. Hence, a firm may ask what features are most important to realize. We approach this question by introducing Value-Based Portfolio Optimization as an addition to common Product Portfolio Scoping approaches that helps deciding on this question.
During software development, many decisions need to be made to guarantee satisfaction of the stakeholders’ requirements and goals. The full satisfaction of all of these requirements and goals may not be possible, requiring decisions over conflicting human interests as well as technological alternatives, with an impact on the quality and cost of the final solution. This work aims at assessing the suitability of multi-criteria decision making (MCDM) methods to support software engineers’ decisions. To fulfil this aim, a HAM (Hybrid Assessment Method) is proposed, which gives its user the ability to perceive the influence different decisions may have on the final result. HAM is a simple and efficient method that combines one single pairwise comparison decision matrix (to determine the weights of criteria) with one classical weighted decision matrix (to prioritize the alternatives). To avoid consistency problems regarding the scale and the prioritization method, HAM uses a geometric scale for assessing the criteria and the geometric mean for determining the alternatives ratings.
Product line engineering is a recent approach to software development that specifically aims at exploiting commonalities and systematic variabilities among functionally overlapping systems in terms of large scale reuse. Taking full advantage of this potential requires adequate planning and management of the reuse approach as otherwise huge economic benefits will be missed due to an inappropriate alignment of the reuse infrastructure. Key in product line planning is the scoping activity, which aims at focussing the reuse investment where it pays. Scoping actually happens on several levels in the process: during the domain analysis step (analysis of product line requirements) a focusing needs to happen just like during the decision of what to implement for reuse. The latter decision also has important ramifications for the development of an appropriate reference architecture as it provides the reusability requirements for this step. We describe an integrated approach that has been developed, improved, and validated over the last few years. The approach fully covers the scoping activities of domain scoping and reuse infrastructure scoping and was validated in several industrial case studies.
Software product lines - practices and patterns
- Clements