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Assisting Mentors in Selecting Newcomers’ Next Task in Software Product Lines: A Recommender System Approach
Abstract
Onboarding (i.e., the process of incorporating new people) is relevant because it introduces employees to their role, the company’s culture, and what the company has to offer. Onboarding is then dependent on the company’s culture and practices. When it comes to software development, these practices include the methods, the tools or the developers’ organigram. Accordingly, there is not a one-size-fits-all onboarding, rather this procedure needs to be tuned for the practice at hand. This work tackles the specifics brought about by Software Product Line Engineering w.r.t. traditional software development, namely: larger code base, larger code variability, and larger and more heterogeneous teams. Specifically, this works advocates for feature-centric onboarding. Features (i.e., functional characteristics that are visible for a user) already play a key role throughout the SPL lifecycle. In this context, we advocate for defining the onboarding process as a journey where milestones are equated with features. Unfortunately, finding the most appropriate feature for a newcomer, if conducted manually by mentors, would be time-consuming, given the sheer number of features. To face this problem, we advocate for Recommender Systems based on the similarity between the feature’s codebase and the code previously explored by the newcomer. To this end, we resort to Topic Modeling, and specifically, Latent Dirichlet Allocation. We provide proof-of-concept through RecomMentor, a recommender system for pure-variants as the variability management system. RecomMentor is put to test against ranking metrics of the Information Retrieval literature. The first evaluation suggests that LDA could be an appropriate technique, paving the way towards using Recommender Systems in feature-based onboarding scenarios.
... The evolution of social networks can be described by analyzing them based on some aspects over time, e.g., shared activities, members' associations, the similarity between individuals' attributes, and the closure of network cycles [Yang et al 2016]. When using machine learning models on evolutionary data, the authors in [Medeiros et al 2022] demonstrated that identifying experts by observing their temporal activity outperforms models that use static data snap-shots. In [Horta et al 2019], a temporal analysis performed on overlapping communities showed that overlapping nodes might be associated with either multidisciplinary developers collaborating on different technologies simultaneously or changing their interests. ...
Este trabalho propõe uma solução para a análise sintática e semântica em redes sociais no contexto do Desenvolvimento Global de Software. Conduzimos três estudos de caso utilizando dados extraídos de GitHub para avaliar a abordagem proposta. Os estudos de caso fornecem indícios de que o nosso método proposto pode identificar especialistas, destacando a sua especialidade e importância para a evolução da rede social.
... Also, while few works aim to find appropriate individuals to help with GSD issues, they are not directly related to the term expert. Instead, they recommend individuals to help with code reviews based on their historical contributions [67] or previously explored code [69]. ...
Social network analysis has been widely used in different application contexts. For example, in Global Software Development, where multiple developers with diverse skills and knowledge are involved, the use of social networking models helps to understand how these developers collaborate. Finding experts who can help address critical elements or issues in a project is a challenging and critical task. It is especially true in the context of Global Software Development projects, where developers with specific skills and knowledge often need to be identified. In this sense, searching for essential members is a valuable task, as they are fundamental to the evolution of the network. This article proposes a broad solution for syntactic and semantic analysis in social networks in the Global Software Development context. In this solution, we define a model for the social network capable of capturing collaboration between developers, incorporate strategies for temporal analysis of the network, explore the network using machine learning algorithms, and propose an ontology to enrich the data semantically. We conducted three case studies using data extracted from GitHub to evaluate the proposed approach. The case studies provide evidence that our proposed method can identify specialists, highlighting their expertise and importance to the evolution of the social network.
The combination of scale and distribution in software projects makes the onboarding of new developers problematic. To the best of our knowledge, there is no research on the relationship between onboarding strategies and the performance evolution of newcomers in large-scale, globally distributed projects. Furthermore, there are no approaches to support the development of strategies to systematically onboard developers. In this paper, we address these gaps by means of an industrial case study. We identified that the following aspects seem to be related to the observed onboarding results: the distance to mentors, the formal training approach used, the allocation of large and distributed tasks in the early stages of the onboarding process, and team instability. We conclude that onboarding must be planned well ahead and should consider avoiding the aspects mentioned above. Based on the results of this investigation, we propose a process to strategize and evaluate onboarding. To develop the process, we used business process modeling. We conducted a static validation of the proposed process utilizing interviews with experts. The static validation of the process indicates that it can help companies to deal with the challenges associated with the onboarding of newcomers through more systematic, effective, and repeatable onboarding strategies.
Although a stable team is deemed optimal for agile project success, new team members need to join ongoing agile projects. Newcomers must rapidly assimilate into the organisational and project environment while learning how to contribute effectively to the project and integrate into the team without seriously interrupting project progress. This paper addresses how newcomers integrate into an established agile project team and the challenges newcomers and the team face during this process. This paper is a single case study of a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. We found a mixture of traditional onboarding practices and specific agile practices contribute to the onboarding process. Onboarding challenges include empowerment and mindset change, accommodating part-timers, conveying agile principles, and adjusting to changes in team composition.
We undertake a detailed examination of the steps that make up offline experiments for recommender system evaluation, including the manner in which the available ratings are filtered and split into training and test; the selection of a subset of the available users for the evaluation; the choice of strategy to handle the background effects that arise when the system is unable to provide scores for some items or users; the use of either full or condensed output lists for the purposes of scoring; scoring methods themselves, including alternative top-weighted mechanisms for condensed rankings; and the application of statistical testing on a weighted-by-user or weighted-by-volume basis as a mechanism for providing confidence in measured outcomes. We carry out experiments that illustrate the impact that each of these choice points can have on the usefulness of an end-to-end system evaluation, and provide examples of possible pitfalls. In particular, we show that varying the split between training and test data, or changing the evaluation metric, or how target items are selected, or how empty recommendations are dealt with, can give rise to comparisons that are vulnerable to misinterpretation, and may lead to different or even opposite outcomes, depending on the exact combination of settings used.
The IT sector struggles with talent acquisition and low retention rates. While several field studies have explored onboarding of software developers, the software engineering literature lacks studies that develop and evaluate theoretical models. This study seeks to explore the link between onboarding of new hires and turnover intention of these professionals. In particular, we develop a theoretical model that identifies a number of onboarding activities, and link these to onboarding success. We then look at what we have termed “organizational fit,” which we define as two aspects of software professionals, namely job satisfaction and the quality of their relationships on the workfloor, and investigate how these mediate the relation between short-term onboarding success and a longer-term intention to leave (or stay with) an organization. We test our model with a sample of 102 software professionals using PLS-SEM. The findings suggest that providing support to new hires plays a major role in onboarding success, but that training is less important. Further, we found that job satisfaction mediates the relationship between onboarding success and turnover intention, but workplace relationship quality does not. Based on the findings, we discuss a number of implications for practice and suggestions for future research.
New employees are supposed to quickly understand their tasks, internal processes and familiarize with colleagues. This process is called “onboarding” and is still mainly realized by organizational methods from human resource management, such as introductory events or special employee sessions. Software tools and especially mobile applications are an innovative means to support provide onboarding processes in a modern, even remote, way. In this paper we analyze how the use of gamification can enhance onboarding processes. Firstly, we describe a mobile onboarding application specifically developed for the young, technically literate generations Y and Z, who are just about to start their career. Secondly, we report on a study with 98 students and young employees. We found that participants enjoyed the gamified application. They especially appreciated the feature “Team Bingo” which facilitates social integration and teambuilding. Based on the OCEAN personality model (“Big Five”), the personality traits agreeableness and openness revealed significant correlations with a preference for the gamified onboarding application.
An influx of newcomers is critical to the survival, long-term success, and continuity of many Open Source Software (OSS) community-based projects. However, newcomers face many barriers when making their first contribution, leading in many cases to dropouts. Due to the collaborative nature of community-based OSS projects, newcomers may be susceptible to social barriers, such as communication breakdowns and reception issues. In this article, we report a two-phase study aimed at better understanding social barriers faced by newcomers. In the first phase, we qualitatively analyzed the literature and data collected from practitioners to identify barriers that hinder newcomers’ first contribution. We designed a model composed of 58 barriers, including 13 social barriers. In the second phase, based on the barriers model, we developed FLOSScoach, a portal to support newcomers making their first contribution. We evaluated the portal in a diary-based study and found that the portal guided the newcomers and reduced the need for communication. Our results provide insights for communities that want to support newcomers and lay a foundation for building better onboarding tools. The contributions of this paper include identifying and gathering empirical evidence of social barriers faced by newcomers; understanding how social barriers can be reduced or avoided by using a portal that organizes proper information for newcomers (FLOSScoach); presenting guidelines for communities and newcomers on how to reduce or avoid social barriers; and identifying new streams of research.
Newcomers’ seamless onboarding is important for open collaboration communities, particularly those that leverage outsiders’ contributions to remain sustainable. Nevertheless, previous work shows that OSS newcomers often face several barriers to contribute, which lead them to lose motivation and even give up on contributing. A well-known way to help newcomers overcome initial contribution barriers is mentoring. This strategy has proven effective in offline and online communities, and to some extent has been employed in OSS projects. Studying mentors’ perspectives on the barriers that newcomers face play a vital role in improving onboarding processes; yet, OSS mentors face their own barriers, which hinder the effectiveness of the strategy. Since little is known about the barriers mentors face, in this paper, we investigate the barriers that affect mentors and their newcomer mentees. We interviewed mentors from OSS projects and qualitatively analyzed their answers. We found 44 barriers: 19 that affect mentors; and 34 that affect newcomers (9 affect both newcomers and mentors). Interestingly, most of the barriers we identified (66%) have a social nature. Additionally, we identified 10 strategies that mentors indicated to potentially alleviate some of the barriers. Since gender-related challenges emerged in our analysis, we conducted nine follow-up structured interviews to further explore this perspective. The contributions of this paper include: identifying the barriers mentors face; bringing the unique perspective of mentors on barriers faced by newcomers; unveiling strategies that can be used by mentors to support newcomers; and investigating gender-specific challenges in OSS mentorship. Mentors, newcomers, online communities, and educators can leverage this knowledge to foster new contributors to OSS projects.
Previous research found that inexperienced software engineers may tend to view automatic testing as a waste of time and as an activity completely separate from programming. This could have a negative impact on their later careers and could be a sign that improvements in software engineering education are needed when it comes to testing. At the same time, this stance could negatively influence the perception that practitioners have of recent university graduates. To explore this issue, we conducted a qualitative study and surveyed 170 and interviewed 22 practitioners about their experiences with recent graduates, focusing on software testing skills. We find that practitioners do recognize a skill gap between university graduates and industry expectations and that this perception could be engrained deeply enough already to influence hiring practices. Practitioners use different and at times costly strategies to alleviate this skill gap, such as training and mentoring efforts. We validated core findings in a survey with 698 professional software developers. Our qualitative insights can help industry, research, and educational institutions guide in-depth studies that explore the severity of the effects we have found. The coping strategies we have found can provide valuable starting points that can inform changes in how we educate the software engineers of the future.
A recommendation system for software engineering (RSSE) is a software application that provides information items estimated to be valuable for a software engineering task in a given context. Present the results of a systematic literature review to reveal the typical functionality offered by existing RSSEs, research gaps, and possible research directions. We evaluated 46 papers studying the benefits, the data requirements, the information and recommendation types, and the effort requirements of RSSE systems. We include papers describing tools that support source code related development published between 2003 and 2013. The results show that RSSEs typically visualize source code artifacts. They aim to improve system quality, make the development process more efficient and less expensive, lower developer’s cognitive load, and help developers to make better decisions. They mainly support reuse actions and debugging, implementation, and maintenance phases. The majority of the systems are reactive. Unexploited opportunities lie in the development of recommender systems outside the source code domain. Furthermore, current RSSE systems use very limited context information and rely on simple models. Context-adapted and proactive behavior could improve the acceptance of RSSE systems in practice.
Context: Onboarding is a process that helps newcomers become integrated members of their organisation. Successful onboarding programs can result in increased performance in conventional organisations, but there is little guidance on how to onboard new developers in Open Source Software (OSS) projects. Goal: In this study, we examine how mentoring and project characteristics influence the effectiveness and efficiency of the onboarding process. We study a collaboration program involving a total of nine Open Source Software projects and more than 120 students from different universities around the world as part of Facebook’s Education Modernization Program. Method: We use quantitative measurements of source code repositories, issue tracking systems, and discussion fora to examine how newcomers become contributing members of their OSS projects. Results: We found that developers receiving deliberate onboarding support through mentoring were more active at an earlier stage than developers entering projects through conventional means. Also, we found that project size and lifetime influenced onboarding. Conclusion: Empirical decision support can contribute to a more effective onboarding process in OSS projects. Mentor support in critical stages can accelerate the process, but project maturity is also a significant factor that increases the effect of onboarding.
A typical entry point for new developers in an open source project is to contribute a bug fix. However, finding an appropriate bug and an appropriate fix for that bug requires a good understanding of the project, which is nontrivial. Here, we extend Tesseract - an interactive project exploration environment - to allow new developers to search over bug descriptions in a project to quickly identify and explore bugs of interest and their related resources. More specifically, we extended Tesseract with search capabilities that enable synonyms and similar-bugs search over bug descriptions in a bug repository. The goal is to enable users to identify bugs of interest, resources related to that bug, (e.g., related files, contributing developers, communication records), and visually explore the appropriate socio-technical dependencies for the selected bug in an interactive manner. Here we present our search extension to Tesseract.
Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible
options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process
performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and
interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items.
This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the
diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the
basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and
drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application
domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most
widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and
future research which might lead towards the next generation of systems, by describing the role of User Generated Content
as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations,
that is to say surprisingly interesting items that they might not have otherwise discovered.
Sociological and technical difficulties, such as a lack of informal encounters, can make it difficult for new members of noncollocated software development teams to learn from their more experienced colleagues. To address this situation, we have developed a tool, named Hipikat that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. After describing the Hipikat tool, we present two studies investigating Hipikat's usefulness in software modification tasks. One study evaluated the usefulness of Hipikat's recommendations on a sample of 20 modification tasks performed on the Eclipse Java IDE during the development of release 2.1 of the Eclipse software. We describe the study, present quantitative measures of Hipikat's performance, and describe in detail three cases that illustrate a range of issues that we have identified in the results. In the other study, we evaluated whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. We describe the study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.
Context
Recruiters and practitioners are increasingly relying on online activities of developers to find a suitable candidate. Past empirical studies have identified technical and soft skills that managers use in online peer production sites when making hiring decisions. However, finding candidates with relevant skills is a labor-intensive task for managers, due to the sheer amount of information online peer production sites contain.
Objective
We designed a profile aggregation tool—Visual Resume—that aggregates contribution information across two types of peer production sites: a code hosting site (GitHub) and a technical Q&A forum (Stack Overflow). Visual Resume displays summaries of developers’ contributions and allows easy access to their contribution details. It also facilitates pairwise comparisons of candidates through a card-based design. We present the motivation for such a design and design guidelines for creating such recruitment tool.
Methods
We performed a scenario-based evaluation to identify how participants use developers’ online contributions in peer production sites as well as how they used Visual Resume when making hiring decisions.
Results
Our analysis helped in identifying the technical and soft skill cues that were most useful to our participants when making hiring decisions in online production sites. We also identified the information features that participants used and the ways the participants accessed that information to select a candidate.
Conclusions
Our results suggest that Visual Resume helps in participants evaluate cues for technical and soft skills more efficiently as it presents an aggregated view of candidate’s contributions, allows drill down to details about contributions, and allows easy comparison of candidates via movable cards that could be arranged to match participants’ needs.
With a volatile labour and technological market,
onboarding is becoming increasingly important. The process
of incorporating a new developer, a.k.a. the newcomer, into a
software development team is reckoned to be lengthy, frustrating
and expensive. Newcomers face personal, interpersonal, process
and technical barriers during their incorporation, which, in turn,
affects the overall productivity of the whole team. This problem
exacerbates for Software Product Lines (SPLs), where their size
and variability combine to make onboarding even more challenging,
even more so for developers that are transferred from the
Application Engineering team into the Domain Engineering team,
who will be our target newcomers. This work presents concept
maps on the role of sensemaking scaffolds to help to introduce
these newcomers into the SPL domain. Concept maps, used as
knowledge visualisation tools, have been proven to be helpful for
meaningful learning. Our main insight is to capture concepts of
the SPL domain and their interrelationships in a concept map,
and then, present them incrementally, helping newcomers grasp
the SPL and aiding them in exploring it in a guided manner while
avoiding information overload. This work’s contributions are
four-fold. First, concept maps are proposed as a representation
to introduce newcomers into the SPL domain. Second, concept
maps are presented as the means for a guided exploration
of the SPL core assets. Third, a feature-driven concept map
construction process is introduced. Last, the usefulness of concept
maps as guides for SPL onboarding is tested through a formative
evaluation.
Link to the online demo: https://rebrand.ly/wacline-cmap
The software system controlling a train is typically deployed on various hardware architectures and is required to process various signals across those deployments. Increases of such customization scenarios, as well as the needed adherence of the software to various safety standards in different application domains, has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected by means of a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss research opportunities, focusing in particular on the areas of product line evolution and test automation.
Pre-Print: http://www.es.mdh.se/publications/5861-
In empirical studies on processes, practices, and techniques of software engineering, automation and machine learning are gaining popularity. In order to extract knowledge from existing software projects, a sort of similarity analysis is often performed using different methodologies, data and metadata. This systematic literature review focuses therefore on existing approaches of similarity-, categorization- and relevance-based analysis on software applications. In total, 136 relevant publications and patents were identified between 2002 and 2019 according to the established inclusion and exclusion criteria, which perform a calculation of software similarity in general or to support certain software engineering phases.
Many companies have turned towards globally distributed software development in their quest for access to more development capacity. This paper investigates how a company onboarded distributed teams in a global project, and report experience on how to study such distributed projects. Onboarding is the process of helping new team members adapt to the existing team and ways of working. The goal of the studied onboarding program was to integrate Por-tuguese developers into two existing Norwegian teams. Further, due to the growing trend in utilizing globally distributed projects, and the challenge of conducting studies in distributed organizations, it is crucial to find good practices for researching such projects. We collected qualitative data from interviews, observations, Slack conversations and documents, and quantitative data on Slack activity. We report experiences on different onboarding practices and techniques, and we suggest guidelines to help other researchers conduct qualitative studies in globally distributed projects. CCS CONCEPTS • General and reference → Empirical studies; • Software and its engineering → Software creation and management.
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.
Ecological validity is a term commonly used in several disciplines to refer to the fact that in a research study, the methods, the materials, and the settings must approximate the real world, i.e. what happens in everyday life. Variability modeling is no exception, it has striven for this form of validity by looking at two main sources, industrial projects and open source projects. Despite their unquestionable value, industrial projects inherently pose limitations; for instance, in terms of open access or results replication, which are two important tenets for any scientific endeavor. In this paper, we present our first findings on the use of open source projects in variability modeling research, and identify trends and avenues for further research.
Augmented reality (AR) is a technological field of study that bridges the physical and digital world together with a view to improving user experience. AR holds great potential to change the delivery of software services or software process improvement by utilizing a specific set of components. The purpose of this exploratory study is to propose an integration framework to support AR for improving the onboarding process, notably in introducing new hires to the development process while performing their daily tasks. In addition, it also aims to enhance the software development workflow process using AR. Similar to a GPS device that can guide you from point A to point B, our goal is to create software artifacts like navigation components where software teams may benefit from digitally enhanced working conditions provided using AR. After conducting a review in the literature, we confirmed that there is lack of studies about the combination of augmented reality with software engineering disciplines for onboarding. In this paper, we formalized our approach based on the benefits of AR. Ultimately; we propose an AR-based preliminary model for improving the software development process.
Categorizing software and detecting similar programs are useful for various purposes including expertise sharing, program comprehension, and rapid prototyping. However, existing categorization and similar software detection tools are not sufficient. Some tools only handle applications written in certain languages or belonging to specific domains like Java or Android. Other tools require significant configuration effort due to their sensitivity to parameter settings, and may produce excessively large numbers of categories. In this paper, we present a more usable and reliable approach of Language-Agnostic Software Categorization and similar Application Detection (LASCAD). Our approach applies Latent Dirichlet Allocation (LDA) and hierarchical clustering to programs’ source code in order to reveal which applications implement similar functionalities. LASCAD is easier to use in cases when no domain-specific tool is available or when users want to find similar software in different programming languages. To evaluate LASCAD's capability of categorizing software, we used three labeled data sets: two sets from prior work and one larger set that we created with 103 applications implemented in 19 different languages. By comparing LASCAD with prior approaches on these data sets, we found LASCAD to be more usable and outperform existing tools. To evaluate LASCAD's capability of similar application detection, we reused our 103-application data set and a newly created unlabeled data set of 5220 applications. The relevance scores of the Top-1 retrieved applications within these two data sets were, separately, 70% and 71%. Overall, LASCAD effectively categorizes and detects similar programs across languages.
Onboarding is the process of supporting new employees regarding their social and performance adjustment to their new job. Software companies have faced challenges with recruitment and onboarding of new team members, and there is no study that investigates it in a holistic way. In this paper, we conducted a multi-case study to investigate the onboarding of software developers/teams, associated challenges, and areas for further improvement in 3 globally distributed legacy projects. We employed Bauer's model for onboarding to identify the current state of the onboarding strategies employed in each case. We learned that the employed strategies are semi-formalized. Besides, in projects with multiple sites, some functions are executed locally, and the onboarding outcomes may be hard to control. We also learned that onboarding in legacy projects is especially challenging and that decisions to distribute such projects across multiple locations shall be approached carefully. In our cases, the challenges to learn legacy code were further amplified by the project scale and the distance to the original sources of knowledge. Finally, we identified practices that can be used by companies to increase the chances of being successful when onboarding software developers and teams in globally distributed legacy projects.
Newcomers in a software development project often need assistance to complete their first tasks. Then a mentor, an experienced member of the team, usually teaches the newcomers what they need to complete their tasks. But, to allocate an experienced member of a team to teach a newcomer during a long time is neither always possible nor desirable, because the mentor could be more helpful doing more important tasks. During the development the team interacts with a version control system, bug tracking and mailing lists, and all these tools record data creating the project memory. Recommender systems can use the project memory to help newcomers in some tasks answering their questions, thus in some cases the developers do not need a mentor. In this paper we present Mentor, a recommender system to help newcomers to solve change requests. Mentor uses the Prediction by Partial Matching (PPM) algorithm and some heuristics to analyze the change requests, and the version control data, and recommend potentially relevant source code that will help the developer in the change request solution. We did three experiments to compare the PPM algorithm with the Latent Semantic Indexing (LSI). Using PPM we achieved results for recall rate between 37% and 66.8%, and using LSI the results were between 20.3% and 51.6%.
We examined how firms' socialization tactics help establish person-organization fit between newcomers and organizations. We used a 3-wave longitudinal design that followed individuals over 2 years: we distributed the first survey before their job search began and the last survey 18 months after their college graduation. Results indicated that newcomers' subjective fit perceptions, as well as changes in their values, were associated with two types of socialization tactics: content (i.e., tactics that are sequential and fixed vs. variable and random) and social aspects (i.e., tactics that emphasize serial and investiture processes rather than disjunctive and divestiture processes). The context dimension of socialization tactics, where socialization is collective and formal (vs. individualized and informal), was not related to P-O fit in this study.
Abstract Approximately 70% of the source code of a software sys- tem consists of identifiers. Hence, the names chosen as identifiers are of paramount,importance,for the readabil- ity of computer,programs,and therewith their comprehen- sibility. However, virtually every programming language allows programmers,to use almost arbitrary sequences of characters as identifiers which far too often results in more or less meaningless,or even misleading naming. Coding style guides address this problem but are usually limited to general and hard to enforce rules like “identifiers should be self-describing”. This paper renders adequate identifier naming far more precisely. A formal model, based on bi- jective mappings between concepts and names, provides a solid foundation for the definition of precise rules for con- cise and consistent naming. The enforcement of these rules is supported by a tool that incrementally builds and main- tains a complete identifier dictionary while the system is being developed. The identifier dictionary explains the lan- guage used in the software system, aids in consistent nam- ing, and improves productivity of programmers by propos- ing suitable names depending on the current context. 1. Naming and Comprehension
Context in industrial software engineering research
- K Petersen
- C Wohlin
Petersen, K., Wohlin, C.: Context in industrial software engineering research. In:
ESEM, pp. 401-404. IEEE Computer Society (2009). https://doi.org/10.1109/
ESEM.2009.5316010