Software Teams Research Articles

A Systematic Review on Non-Functional Requirements Documentation in Agile Methodology

This systematic literature review studies and summarizes findings of requirements documentation of non-functional requirements practiced by agile software development teams. It identifies current practices and existing gaps when agile software teams discuss and implement non-functional requirements, and the current methods used to document non-functional requirements. Our aim is to identify available evidence on current practices and gaps in documenting non-functional requirements. The review was conducted by searching major databases for publications between 2018 and 2022. The inclusion and exclusion criteria as well as quality assessment scoring criteria were subsequently established. Results show that common themes in the practices and gaps in requirements documentation by agile teams do intersect although they are scarce. This review has the benefit for practitioners to have a better approach to document the requirements that may result in a more quality software product.

A Survey of Software Metrics for Transitioning to an Updated Version of the Current Existing Framework or a New Framework

A software framework is an abstraction that allows to selectively updating software that provides generic functionality by adding additional code written by the user. Basically, a physical or conceptual structure is used to support or guide the construction of something that grows the structure into something productive. Many effective software teams start with frameworks as their foundation. Bundled code libraries, software modeling, APIs, and a myriad of other capabilities, which are included in these frameworks, make programming considerably faster and more effective. Therefore, software engineers often delve into various frameworks for development and other purposes. A survey has been conducted and opinions from teachers, students, and developers were solicited. This paper examines the results of the poll and tries to explain what software engineers, students, or seasoned developers experience when switching to a new framework in the middle of a project.

Mapping and size estimation of people who inject drugs: an innovative approach for targeted harm reduction program in Iran

ABSTRACT Background Hotspot mapping of People Who Inject Drugs (PWIDs) is important for harm reduction program planning. The aim of this study is to estimate the numbers of PWIDs and their geographic distribution in Iran. Methods We used the hotspot mapping and size estimation method to estimate the number of drug users in four cities of Iran from December 2017 to January 2019. We calculated three estimates in each hotspot: a) Key Affected Population Key Informant (KAP-KI) estimate (drug users met in the hotspot), b) Non-KAP KI estimate (e.g., taxi drivers, shopkeepers nearby the hotspot); c) estimation made by field observers. Locations of hotspots were recorded by the field team and ArcGIS software was used for mapping. Results A total number of 335 hotspots were identified in four cities and 1524 key informants were interviewed. The forty four per cent of identified hotspots received harm reduction services. Ahvaz had the highest estimated number of PWIDs (843, range: 703–887), followed by Sari (346, range: 307–666), Yazd (228, range: 221–471) and district 2 of Tehran (18, range: 18–34). Conclusion Findings highlight the need to use hotspot mapping to identify new PWIDs hotspots and best location for drop-in centers (DIC) and to provide targeted harm reduction programs.

Assessing individual contributions to software engineering projects: a replication study

ABSTRACT Background and Context Assessing team members’ indivdiual contributions to software development projects poses a key problem for computing instructors. While instructors typically rely on subjective assessments, objective assessments could provide a more robust picture. To explore this possibility, In a 2020 paper, Buffardi presented a correlational analysis of objective metrics and subjective metrics in an advanced software engineering project course (n= 41 students and 10 teams), finding only two significant correlations. Objective To explore the robustness of Buffardi’s findings and gain further insight, we conducted a larger scale replication of the Buffardi study (n = 118 students and 25 teams) in three courses at three institutions. Method We collected the same data as in the Buffardi study and computed the same measures from those data. We replicated Buffardi’s exploratory, correlational and regression analyses of objective and subjective measures. Findings While replicating four of Buffardi’s five significant correlational findings and partially replicating the findings of Buffardi’s regression analyses, our results go beyond those of Buffardi by identifying eight additional significant correlations. Implications In contrast to Buffardi’s study, our larger scale study suggests that subjective and objective measures of individual performance in team software development projects can be fruitfully combined to provide consistent and complementary assessments of individual performance.

EveryBOTy Counts: Examining Human-Machine Teams in Open Source Software Development.

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human-machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human-machine teaming research.

The main pillars of Agile consolidation in newly Agile teams in Agile software development

<span>Many software companies and teams use Agile methods as their main development approach. These methods promise higher team productivity, faster product delivery, a more flexible development process, and greater customer satisfaction. Nevertheless, a review of the literature shows that adapting to these methods, known as Agile transition, is not as easy as expected. However, several frameworks and models have been proposed to facilitate the Agile transition process. The challenging issue after the transition to agility is the behavior of companies and teams after the Agile transition and how to maintain agility in the long run. Very little research has been done on this issue, which has largely expressed concern. The present study tries to explore the hidden aspects of the transition to agility and provide a solution for Agile consolidation in newly Agile software teams. In this regard, using the grounded theory approach, the basic theory of Agile consolidation in these teams has been presented. Preliminary findings of the study indicate important factors that play an important role in Agile consolidation. Identification of challenges, facilitators, organizational culture structure, and human roles in Agile consolidation is the most important initial findings of this study.</span>

Challenges of and Opportunities for a Large Diverse Software Team

A large software team consisting of members with different expertise, skillsets, personalities, ethnicities, and involving collaboration on a large and complex software product presents many technical and cultural challenges, but also provides unique opportunities. In this article, we discuss the essential issues we faced when successfully transforming a collection of various independently developed software libraries into one large integrated product: the eXtreme-scale scientific Software Development Kit (xSDK). We argue it is just as important to pay attention to cultural challenges, such as establishment of reliable communication channels that considers, among others, differences in personalities and backgrounds as well as overcoming geographical separation and time-zone distribution when collaborating, as technical challenges. Finally, we discuss opportunities stemming from participating in a large diverse software team, such as increased internal expertise, variety of skillsets, broadened connections to external experts, and access to a larger pool of ideas or solutions.

Maximizing integrative learning in software development teams: A systematic review of key drivers and future research agenda

Software development is a complex phenomenon that requires software teams to integrate diverse knowledge and expertise to provide innovative solutions. A critical success factor for software teams is their integrative learning capability which is driven by multiple factors. We systematically review and synthesize extant literature to identify critical antecedents of integrative learning in software teams and uncover knowledge gaps to inform future research. Searching multiple databases, 447 papers were identified, of which 32 were selected for the final analysis after a rigorous data extraction process. We performed open and axial coding and affinity diagramming to thematically analyze each study for antecedents associated with integrative learning. We also triangulated our results by content analyzing the studies using a software-based text analysis tool. The findings indicate five crucial drivers of integrative learning in software teams: social harmony, process agility, team design, technological maturity, and project environment. The study also highlights the value of recognizing the inter-team and intra-team contexts in team learning. Based on the study findings, we propose a model of integrative learning in software teams, discuss future research agendas, and offer practical insights and recommendations to software professionals.

Social-technical network effects in open source software communities: understanding the impacts of dependency networks on project success

Purpose To better understand the success of an open source software (OSS) project, this study aims to examine the role of social dependency networks (i.e. social and technical dependencies) in online communities.Design/methodology/approach This study focuses on dependencies using three network metrics – degree centrality, betweenness centrality and closeness centrality – in developer and module networks. A longitudinal analysis from the projects hosted at Sourceforge.net is conducted to examine the effects of social and technical networks on the success of OSS projects. To address our research questions, we have constructed research models to investigate the social network effects in developer networks, the technical network effects in module networks, and the social-technical network effects in both types of networks.Findings The results reveal nonlinear relationships between degree centrality in both social and technical networks and OSS success, highlighting the importance of a moderate level of degree centrality in team structure and software architecture. Meanwhile, a moderate level of betweenness centrality and a lower level of closeness centrality between developers lead to a higher chance of OSS project success.Originality/value This study is the first attempt to consider the network metrics in both module networks of the technical sub-system and developer networks of the social sub-system to better understand their influences on project success.

Becoming agile together: Customer influence on agile adoption within commissioned software teams

Despite the ongoing expansion of agile practices beyond software firms, agile adoption remains risky, challenging, and poorly understood. Although agile practices emphasize self-organization and customer collaboration, we know little about how customers influence agile adoption within self-organizing teams. Here, we analyze how a commissioned software team engaged in customer collaboration during agile adoption at a Danish IT service provider. Our case study shows that the software team's transition to self-organized teamwork practices, agile planning routines, and active customer engagement was mutually dependent on the customer's trust in the software team and flexible collaborative routines. As a result, we advance a theoretical perspective of customer influence on agile adoption within commissioned software teams, implying that both software teams and customers need to navigate a contradictory tension between self-organization and collaboration to become agile together.

Search-based fairness testing for regression-based machine learning systems

ContextMachine learning (ML) software systems are permeating many aspects of our life, such as healthcare, transportation, banking, and recruitment. These systems are trained with data that is often biased, resulting in biased behaviour. To address this issue, fairness testing approaches have been proposed to test ML systems for fairness, which predominantly focus on assessing classification-based ML systems. These methods are not applicable to regression-based systems, for example, they do not quantify the magnitude of the disparity in predicted outcomes, which we identify as important in the context of regression-based ML systems.Method:We conduct this study as design science research. We identify the problem instance in the context of emergency department (ED) wait-time prediction. In this paper, we develop an effective and efficient fairness testing approach to evaluate the fairness of regression-based ML systems. We propose fairness degree, which is a new fairness measure for regression-based ML systems, and a novel search-based fairness testing (SBFT) approach for testing regression-based machine learning systems. We apply the proposed solutions to ED wait-time prediction software.Results:We experimentally evaluate the effectiveness and efficiency of the proposed approach with ML systems trained on real observational data from the healthcare domain. We demonstrate that SBFT significantly outperforms existing fairness testing approaches, with up to 111% and 190% increase in effectiveness and efficiency of SBFT compared to the best performing existing approaches.Conclusion:These findings indicate that our novel fairness measure and the new approach for fairness testing of regression-based ML systems can identify the degree of fairness in predictions, which can help software teams to make data-informed decisions about whether such software systems are ready to deploy. The scientific knowledge gained from our work can be phrased as a technological rule; to measure the fairness of the regression-based ML systems in the context of emergency department wait-time prediction use fairness degree and search-based techniques to approximate it.

Development of a Digital Support Application With Evidence-Based Content for Sustainable Return to Work for Persons With Chronic Pain and Their Employers: User-Centered Agile Design Approach.

BackgroundPersons with chronic pain experience a lack of support after completing rehabilitation and the responsibility for the return-to-work (RTW) process is taken over by the employer. In addition, employers describe not knowing how to support their employees. Smartphone apps have been increasingly used for self-management, but there is a lack of available eHealth apps with evidence-based content providing digital support for persons with chronic pain and their employers when they return to work.ObjectiveThis study aims to describe the development of a digital support application with evidence-based content that includes a biopsychosocial perspective on chronic pain for sustainable RTW for persons with chronic pain and their employers (SWEPPE [Sustainable Worker Digital Support for Persons With Chronic Pain and Their Employers]).MethodsA user-centered agile design approach was applied. The multidisciplinary project team consisted of health care researchers, a user representative, and a software team. A total of 2 reference groups of 7 persons with chronic pain and 4 employers participated in the development process and usability testing. Mixed methods were used for data collection. The design was revised using feedback from the reference groups. The content of SWEPPE was developed based on existing evidence and input from the reference groups.ResultsThe reference groups identified the following as important characteristics to include in SWEPPE: keeping users motivated, tracking health status and work situation, and following progress. SWEPPE was developed as a smartphone app for the persons with chronic pain and as a web application for their employers. SWEPPE consists of six modules: the action plan, daily self-rating, self-monitoring graphs, the coach, the library, and shared information with the employer. The employers found the following functions in SWEPPE to be the most useful: employees’ goals related to RTW, barriers to RTW, support wanted from the employer, and the ability to follow employees’ progress. The persons with chronic pain found the following functions in SWEPPE to be the most useful: setting a goal related to RTW, identifying barriers and strategies, and self-monitoring. Usability testing revealed that SWEPPE was safe, useful (ie, provided relevant information), logical, and easy to use with an appealing interface.ConclusionsThis study reports the development of a digital support application for persons with chronic pain and their employers. SWEPPE fulfilled the need of support after an interdisciplinary pain rehabilitation program with useful functions such as setting a goal related to RTW, identification of barriers and strategies for RTW, self-monitoring, and sharing information between the employee and the employer. The user-centered agile design approach contributed to creating SWEPPE as a relevant and easy-to-use eHealth intervention. Further studies are needed to examine the effectiveness of SWEPPE in a clinical setting.

Knowledge activities applied | towards a holistic knowledge management approach in the software industry

PurposeThis paper aims to close the gap between the generic concept of knowledge activities (KAs) and implementing them in the context of software engineering organisations concentrating on the non-technical aspects, such as team organisation and practices.Design/methodology/approachThis qualitative research used a questionnaire with practitioners such as software developers and team leads who were asked to provide feedback on a set of team practices and measures typically used in software engineering projects and assess their relation to the activities of acquiring, codifying, storing, maintaining, transferring and creating knowledge. The obtained results were analysed using frequency analysis and further descriptive statistics yielding a matrix linking the investigated team practices and measures to KAs.FindingsTeam practices and measures commonly applied in software engineering can be facilitated to trigger particular KAs. While most of these team practices and measures originate from agile methods, they are not restricted to these. A purposeful composition can help in assembling a balanced set of KAs aimed at fostering given knowledge goals in software engineering organisations.Practical implicationsBy bridging the communication and terminology gap between knowledge management research and software engineering practitioners, this work lays the foundation for assessing software teams’ knowledge profiles more easily and creating prerequisites for implementing knowledge management by facilitating common practices and measures often already part of their daily work. Hence, overhead can be avoided when implementing knowledge management.Originality/valueTo the best of the authors’ knowledge, this is the first study investigating application and relevance of KAs in the software industry by linking them to practices and measures well-accepted in software engineering, thus providing the necessary vocabulary for the implementation of knowledge management in software development teams.

“No Girls on the Software Team”: Internship Experiences of Women in Computer Science

Despite computer science (CS) students’ widespread participation in internships, few studies have examined how internship experiences impact career decision-making. Because women are severely underrepresented within CS, understanding how internship experiences impact career decision-making can provide critical insight into women’s career development processes and longevity in the field. Using the concept of career self-management within social cognitive career theory as a theoretical framework and standpoint feminism as a critical lens, we conducted interviews with 13 women CS majors to learn how internship experiences influenced their career decisions. Findings reveal three major career development process themes, including questioning technical competence, navigating gendered “microclimates,” and reflecting on careers in CS. While internships largely affirmed women’s career interests in CS, participants also navigated challenging gendered dynamics and often made career decisions directly influenced by these experiences. Findings inform how CS educators, career development practitioners, and employers may better develop inclusive internship cultures in computing.

Reactive Black 5 Removal with Ozone on Lab-scale and Modeling

ABSTRACT This study investigates modeling the ozone removal of the Reactive Black 5 (RB5) dye from an aqueous solution using a combination of nonlinear regression (NLR), multiple linear regression (MLR), and Intrinsically multiple linear regression (IMLR) models. Lack of use and evaluation of the IMLR method in estimating RB5 removal by ozonation attract attention. Experimental data were used in the R Core Team software for the development of the models and estimate of RB5 removal by ozone. The effects of variables such as pH, contact time, initial dye concentration, and applied ozone dosage on RB5 removal by ozone were investigated. Maximum 92% RB5 removal rate was obtained at pH 8, 60 min contact time, 100 mg/L initial RB5 concentration, and applied ozone dosage of 66 mgO3/L. Under these conditions, the amount of specific ozone consumption was 0.678 gO3/gRB5. In order to compare the models, coefficient of determination (R2) and mean square error (MSE) were utilized as reliability and precision criteria. The best R2 and MSE values for the IMLR model were calculated as 0.8940 and 0.098, respectively. To determine the appropriate model and coefficients, analysis of variance (ANOVA), and t-test were used, respectively. Whether the model is within the confidence interval was determined by the significance value (p) and the variation was <5% for the IMLR model. As a result, it was found that the best method for modeling RB5 removal from aqueous solution by ozone was the IMLR method. Detailed explanations on results were introduced in the study.

Complementary and Substitutive Roles of Information Technology in the Relationship between Project Characteristics and Knowledge Integration in Software Teams

ABSTRACT Software development requires the assimilation of team members’ diverse knowledge, ideas, and skills for innovative solutions. A software team’s knowledge integration is affected by project characteristics, such as scale and interdependence, and the team’s use of information technology (IT). We examine how contingencies embedded in these dimensions influence team’s knowledge integration. We argue and show that IT-use plays either complementary or substitutive role in moderating the impacts of project scale and project interdependence on knowledge integration.

Improving Teamwork in Agile Software Engineering Education: The ASEST+ Framework

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents agile software engineers stick together (ASEST+), an improved version of a framework called ASEST that aims to develop team cohesion, leading to better team learning and software engineering student teams. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Effective teamwork is crucial for agile software development’s success and is, therefore, a key topic of current software engineering education. In the previous work, a preliminary proposal for ASEST+ was presented. Here, an improved version, more suitable for agile practice education and considering cohesion antecedents, is described. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcome:</i> A teaching-learning framework to support teamwork in agile software education. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Application Design:</i> ASEST+ is built around Scrum teams and combines learning strategies to train students in collaborative and technical agile practices. ASEST+ establishes policies for role allocation and team rule agreements to regulate communication and address conflict management agile practices. ASEST+ addresses personality traits, conflict resolution, and task interdependence as the antecedents identified as the most important. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> A quasiexperiment showed that the use of ASEST+ significantly increases the students’ positive perceptions on team cohesion, team performance, and team learning compared with the control group.

Predicting the objective and priority of issue reports in software repositories

Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team’s effort. An issue report is a rich source of collaboratively-curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub’s top 1000 repositories do not have any labels. In this work, we aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and state-of-the-art text classifiers. To the best of our knowledge, we are the first to fine-tune a Transformer for issue classification. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with $$82\%$$ (fine-tuned RoBERTa) and $$75\%$$ (Random Forest) accuracy, respectively. Moreover, we conducted human labeling and evaluation on unlabeled issues from six unseen GitHub projects to assess the performance of the cross-project model on new data. The model achieves $$90\%$$ accuracy on the sample set. We measure inter-rater reliability and obtain an average Percent Agreement of $$85.3\%$$ and Randolph’s free-marginal Kappa of 0.71 that translate to a substantial agreement among labelers.

A Quantitative Assessment of the Impact of Homogeneity in Personality Traits on Software Quality and Team Productivity

Software industry is increasingly focusing on the improvement of software quality and team performance to survive in a competitive and rapidly growing environment. Previous studies reveal the importance of social and human factors in software engineering. Despite the importance of these personality factors, only a handful of empirical studies have quantitatively evaluated the impact of personality traits on software quality and effectiveness of software engineering teams. This study quantifies the abstract notion of trait-wise team homogeneity based on the Five-Factor Model (FFM). Additionally, this study also aims to measure its impact on team productivity and software quality. Trait-wise team homogeneity measures the Team Homogeneity Index (THI) for each of the five personality traits of FFM. Therefore, five new metrics i.e. Openness THI (O-THI), Conscientiousness THI (C-THI), Extraversion THI (E-THI), Agreeableness THI (A-THI), and Neuroticism THI (N-THI) are proposed. The utility of these five metrics is evaluated by conducting experiments in academic and industrial environments for three different phases of the Software Development Life Cycle (SDLC) i.e. analysis and design, implementation, and testing. During the analysis and design phase, it was observed that teams (composed of either students or professionals) with greater values of C-THI and A-THI produced better quality design models and teams with greater values of C-THI were more productive. In the implementation phase, teams with higher values of O-THI, C-THI, and E-THI were noticeably more productive and produced better quality code. For the testing phase, teams with greater values of C-THI and A-THI produced better quality test cases and were more productive. Results obtained so far indicate that the newly proposed five metrics – O-THI, C-THI, E-THI, A-THI, and N-THI – appear to have the potential to aid managers and academics alike in building productive teams which can produce better quality software.

On a Preliminary Theory of Communication in Distributed Software Development

Communication is one of the leading challenges faced by teams working in a distributed setting; yet, little has been theorized about how communication occurs in such context. Our long-term research goal is to construct a Communication Theory in Distributed Software Development, aiming to propose a theoretical foundation for future academic studies on the topic of communication and a reference for industry practitioners. To achieve this goal, we are using Grounded Theory, including an Exploratory Literature Review before the theory construction, to confirm the research gap. In this paper, we present a further preliminary version of the Communication Theory comprising six theoretical categories and 31 subcategories. The theory brings, up to know, a consolidated body of knowledge and points out the main concepts that define what communication is in distributed software teams.