Software organizations of all sizes are affected by the Technical debt phenomenon. Large organizations, however, are more prone to their non-technical aspects due to a large number of teams of different sizes, heterogeneity of expertise, and the need for continuous management and communication. This is even more emphasized in large agile software development contexts due to a focus on personal interactions and team collaboration, which consequently poses a challenge due to a significant increase in communication pathways. The goal of this study is to investigate non-technical aspects of technical debt in the context of large-scale agile software development. To achieve this objective, a case study involving four international companies was conducted. During the study, 24 experts were interviewed, and their responses were analyzed using a qualitative research approach. The analysis resulted in five non-technical aspects, which are: social dynamics, process, people, documentation, and requirements aspects, and their indicators, which are specific in a large-scale agile context. The study findings suggest that lack of communication, collaboration, and cooperation are the key contributors to identified debt aspects, technical debt accumulation through these non-technical aspects, and that many of the causes stem from a culture of not developing rules, protocols, or guidelines.

Agile software development (ASD) methods have increasingly been used in large-scale software development projects. While ASD emphasizes the importance of social interactions between practitioners for continuous reflection and knowledge sharing, these learning activities become incredibly challenging in large-scale projects. Drawing on well-established theoretical concepts, we posit that learning in large-scale ASD projects requires a suitable environment that empowers practitioners to openly and frequently engage in social interactions necessary for reflection and knowledge sharing. We hypothesize that several team-level factors shape individuals’ perceptions about the learning environment and learning activities in their projects and ultimately influence their learning behavior. To test our model, we collected survey responses from practitioners working in large-scale ASD projects in five Swedish companies (N=159). The data was analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM). The results show that team structure plays a crucial role in promoting team cohesion and reflexivity, which, alongside knowledge sharing, contribute to learning. Our study provides ASD development research with a theoretically informed understanding of the interrelationship between the learning environment and learning activities in large-scale ASD. Our results guide practitioners in fostering suitable learning environments and enhancing learning in large-scale ASD.

Process debt (PD) in agile software development represents inefficiencies that undermine team performance and job satisfaction. This study investigates the quantitative impact of PD on job satisfaction within agile teams, surveying 191 participants from two software development organizations. Our research examines five PD types: Process Unsuitability, Roles Debt, Synchronization Debt, Documentation Debt, and Infrastructure Debt. Using multiple regression analysis, our model explains approximately 33.8 % of the variance in job satisfaction. Among the five PD types, Process Unsuitability and Roles Debt emerged as statistically significant predictors of reduced job satisfaction. These findings indicate that certain forms of PD have a measurable negative impact on developers' perceptions of their work environment. By identifying which PD types most strongly influence job satisfaction, this research offers empirically grounded insights that can inform targeted interventions. Understanding and addressing the most impactful PD categories may help organizations refine agile processes, thereby mitigating the detrimental effects of process inefficiencies on job satisfaction.

ABSTRACT Agile methods have become a standard practice within software industry, with organizations increasingly adopting large‐scale agile (LSA) frameworks. However, as these frameworks are implemented across multiple teams and organizational functions, new challenges emerge, particularly in maintaining alignment, coherence, and collaboration across teams. One crucial element in addressing these challenges is fostering of a culture of continuous learning and psychological safety, with the objective of optimizing team performance and ensuring project success. Despite the importance of this topic, there is a significant gap in existing literature regarding antecedents of psychological safety and its impact on team learning and performance in LSA environments. This study aims to investigate impact of high‐quality relationships and psychological safety on learning from failures and, consequently, on team performance in LSA context. An online survey of 167 software professionals in Sweden was conducted to test a conceptual model that is developed based on existing literature. The hypotheses were analyzed using partial least squares structural equation modeling. The results demonstrate strong positive correlation between the presence of high‐quality relationships, psychological safety, and capacity to learn from failures and team performance. Specifically, the formation of high‐quality relationships has been demonstrated to significantly enhance psychological safety, which in turn facilitates learning from failures and leads to improved team performance. These findings offer valuable insights for both practitioners and researchers, highlighting the importance of cultivating relational dynamics and a psychologically safe environment in LSA projects. Furthermore, the study offers guidance for future research, regarding the scalability and generalizability of these findings.

Deep learning-based models have revolutionized medical diagnostics by using Big Data to enhance disease diagnosis and clinical decision-making. However, their significant computational demands and opaque decision-making processes, often characterized as "black-box" systems, pose major challenges in time-critical and resource-constrained healthcare settings. To address these issues, this study explores the application of randomized machine learning models, specifically Extreme Learning Machines (ELMs) and Random Vector Functional Link (RVFL) networks, in medical diagnostics. These models introduce stochasticity into their training processes, reducing computational complexity and training times while maintaining accuracy. Furthermore, we integrate Explainable AI techniques namely Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to explain the decision-making rationale of ELMs and RVFL. Performance evaluations on genitourinary cancers and coronary artery disease datasets demonstrate that RVFL outperforms traditional deep learning models, achieving superior accuracy of 88.29% with a computational overhead of 6.22 seconds for genitourinary cancers, and an accuracy of 81.64% with a computational time of 0.0308 seconds for coronary artery disease. This research highlights the potential of randomized models in enhancing efficiency and transparency in medical diagnosis, thereby accelerating better treatment outcomes and advocating for more accessible and interpretable AI solutions in healthcare.

This paper explores the relationship between self-organisation and tailored roles in large agile software development teams. The case study examines a Swedish IT company that has introduced the role of “Security Master” in Scrum teams. The teams are developing a very large and secure 5G solution. Twenty semi-structured interviews were conducted and deductively analysed. The results shed light on the Security Master role, its need, responsibilities and impact on secure coding. The paper concludes with a discussion of lessons learned and recommendations for future research in the context of large, security-sensitive projects.

Scaling agile approaches in large company context is prone to technical debt due to large number of teams of different size, level of expertise and their need for management and communication. The goal of this study is to investigate the phenomenon of non-technical debt related issues in the context of large-scale agile software development. To achieve this goal, eleven experts from two multinational companies were inter-viewed as part of the the case study. The analysis results revealed four non-technical aspects of technical debt that are present in large-scale agile context. These are people, social, documentation and process debt aspects. Furthermore, the findings suggest that lack of communication, collaboration and cooperation are the key contributors to identified debt aspects, and that many of the causes for debts are stemming from a culture of not developing rules, protocols, or guidelines. Implementing ground rules to improve quality seems to mitigate several of the identified debt types.

Background: Agile methodologies emphasise iterative development, customer collaboration, and flexibility in software development. However, challenges arise when agile practices are adopted in larger projects. Process inefficiencies and redundancies, known as process debt, result from the compounded complexities of expanding agile processes and workflows. However, strategies to understand and tackle it remain markedly inadequate.

Abstract Context: The software engineering researchers and practitioners echoed the need for investigations to better understand the engineers developing software and services. In light of current studies, there are significant associations between the personalities of software engineers and their work preferences. However, limited studies are using psychometric measurements in software engineering. Objective: We aim to evaluate attitudes of early‐stage software engineers and investigate link between their personalities and work preferences. Method: We collected extensive psychometric data from 303 graduate‐level students in Computer Science programs at four Pakistani and one Swedish university using Five‐Factor Model. The statistical analysis investigated associations between various personality traits and work preferences. Results: The data support the existence of two clusters of software engineers, one of which is more highly rated across the board. Numerous correlations exist between personality qualities and the preferred types of employment for software developers. For instance, those who exhibit greater levels of emotional stability, agreeableness, extroversion, and conscientiousness like working on technical activities on a set timetable. Similar relationships between personalities and occupational choices are also evident in the earlier studies. More neuroticism is reported in female respondents than in male respondents. Higher intelligence was demonstrated by those who worked on the “entire development process” and “technical components of the project.” Conclusion: When assigning project tasks to software engineers, managers might use the statistically significant relationships that emerged from the analysis of personality attributes. It would be beneficial to construct effective teams by taking personality factors like extraversion and agreeableness into consideration. The study techniques and analytical tools we use may identify subtle relationships and reflect distinctions across various groups and populations, making them valuable resources for both future academic research and industrial practice.

Abstract In software engineering, technical debt (TD) has been widely investigated, but debt regarding social issues, people, and processes has not been explored as much. It should be noted here that we use nontechnical debt (NTD) as an umbrella term to cover social, process, and people debts. Although the number of studies on NTD in software is increasing, the majority of them are descriptive rather than rigorous, and there is no systematic development of cumulative knowledge. As a result, identifying the fundamental causes of NTD and the associated mitigation techniques in software engineering is challenging. Therefore, this study investigates the scientific evidence regarding NTD till date by conducting a systematic mapping review of software engineering research between January 2000 and October 2021. The search strategy resulted in 175 studies, 17 of which were identified as unique and relevant primary papers. The primary studies show that NTD and TD are inextricably linked. In addition, this study also captured a plethora of causes and mitigation strategies for managing NTD and thus makes four important contributions: (i) highlighting state‐of‐the‐art NTD research; (ii) identification of the reported causes and mitigation strategies in the primary papers; and (iii) determination of opportunities for future NTD research.

Abstract Agile methodologies have emerged as transformative paradigms in the ever-evolving software development landscape, emphasizing iterative development, customer collaboration, and adaptability. As the scope and complexity of projects and organizations expand, applying agile principles within the context of Large-Scale Agile Development (LSAD) encounters distinctive challenges. The majority of challenges encountered in LSAD, technical and non-technical, are attributed to the accrual of social debt. However, a conspicuous gap remains in understanding and addressing social debt in LSAD. This study aims to fill this void by investigating social debt in LSAD through an in-depth industrial case study with a leading Nordic company specializing in telecommunications software and services and focusing on producing secure 5G network solutions. The study investigates the causes of LSAD’s social debt and examines its impacts on secure 5G telecom software development. By addressing these objectives, this research sheds light on a critical aspect of LSAD’s social debt, caused by 3C challenges(communication, coordination and collaboration), social confines challenges, community smells challenges, and organisational social challenges in the telecom sector that have been underrepresented in the existing literature.

This research paper examines the implementation and impact of customised roles in large-scale agile software development teams, specifically within the Scrum framework. This is a single case study based on 15 practitioners' interviews from a Swedish software company to review the dynamics of role customisation. The two customised roles of Product Guardians and Security Masters are crucial for addressing complex secure software development, maintaining product quality and security, and fostering team self-organisation. The study also uncovers challenges associated with role customisation, such as the potential overburdening of individuals and disruption of selforganisation. The findings contribute to the discourse on agile methods' adaptability and provide practical insights for organisations considering similar role customisations. Furthermore, the research opens the door for future exploration of organization-wide roles that promote selforganisation.

Psychological safety has been hypothesised as an important antecedent of the success of agile software development (ASD) teams.However, there is a lack of investigation on psychological safety in large-scale agile (LSA) software development teams.This study explored the antecedents and effects of psychological safety on LSA teams.We conducted semistructured interviews with software professionals working on LSA project in a Scandinavian technology company.The results suggest that building a psychologically safe environment is a multi-dimensional factor that requires proactive leadership approach, open communication and constructive feedback.The focus should be on designing teams for learning, remuneration safety, and a well-prepared onboarding process for new team members.A psychologically safe environment contributes to effective teamwork, work satisfaction, and promotion of learning.Absence of such an environment leads to brain drain, highlighting the consequences of neglecting this essential aspect of organisational culture.Future research directions are proposed in this paper.

Trust is an important factor that contributes to citizens' willingness to continuance use of e-gov services. However, there is a lack of prior investigation about trust and continuance use of e-gov services in Pakistan - a developing country. We propose a model to investigate citizens' trust and e-gov services' continuous use intention to fill this research gap. Our study collected data from an online survey of 558 Pakistani citizens. Using partial least squares analysis, we found that disposition to trust positively correlates with both internet and government trust. Moreover, citizen satisfaction, trust, perceived usefulness, confirmation, and perceived risk all have significant impacts on the continuous use intention of e-gov services. This research extends and validates the Expectation-Confirmation Model by exploring key factors that influence e-gov continuance use intention. As such, our study offers valuable insights for policymakers and practitioners involved in e-gov service delivery in developing countries like Pakistan. The paper also discusses our findings' implications and identifies future research directions.

Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

The internet of things is used as a demonstrative keyword for evolution of the internet and physical realms, by means of pervasive distributed commodities with embedded identification, sensing, and actuation abilities. Imminent intellectual technologies are subsidizing internet of things for information transmission within physical and autonomous digital entities to provide amended services, leading towards a new communication era. Substantial amounts of heterogeneous hardware devices, e.g., radio frequency identification (RFID) tags, sensors, and various network protocols are exploited to support object identification and network communication. Data generated by these digital objects is termed as “Big Data” and incorporates high dimensional space with noisy, irrelevant, and redundant features. Direct execution of mining techniques onto such kind of high dimensionality attribute space can increase cost and complexity. Data analytic mechanisms are embedded into internet of things to permit intelligent decision-making capabilities. These notions have raised new challenges regarding internet of things from a data and algorithm perspective. The proposed study identifies the problem in the internet of things network and proposes a novel cuckoo search-based outdoor data management. The technique of the feature extraction is used for the extraction of expedient information from raw and high-dimensional data. After the implementation for the cuckoo search-based feature extraction, few test benchmarks are introduced to evaluate the performance of mutated cuckoo search algorithms. The consequential low-dimensional data optimizes classification accuracy along with reduced complexity and cost.

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018.We collected data from Scopus database, finding 789 XP papers.We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data.The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once.Mining of XP conference paper titles and abstracts result in these hot research topics: "Coordination", "Technical Debt", "Teamwork", "Startups" and "Agile Practices", thus strongly focusing on practical issues.The results also highlight the most influential researchers and institutions.The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.

Software start‐ups are aiming to develop cutting‐edge software products under highly uncertain conditions, overcoming fast‐growing markets under multiple influences. This study aims to identify and analyse the existing scientific literature regarding software development methodologies and practices in software start‐ups published between January 2006 and December 2017 using the systematic mapping study. The results identified 37 relevant primary studies out of 1982 papers. To validate the results from the mapping study, an empirical study was based on the research data collected from 14 real‐life software start‐ups located in Finland, Italy and Norway. The result shows that Agile and Lean start‐up methodologies are the most commonly used in software start‐ups due to the flexible nature and easy tailoring. A total of 144 software development work practices are extracted from the primary studies. This study contributes to the research in several ways: (i) provides state of the art regarding software development methods and practices in software start‐up contexts, (ii) reports commonly used methods along with its benefits identified in primary studies and (iii) identifies opportunities for future software start‐up research.

Communication plays an important role in Agile Software Development (ASD).In each ASD practice (e.g., standup or retrospective meetings), different communication practices and channels are adopted by different companies.Several works have analyzed the impact of communication channels and practices.However, there are no secondary studies summarizing their impact on ASD.This study presents a Systematic Mapping Study (SMS) that aggregates, summarizes, and discusses the results of 25 relevant primary studies concerning the impact of communication channels and practices in ASD.We followed the well-known systematic mapping methodology in software engineering and analyzed empirical studies published before the end of June 2018.The results of our study have yielded several strategies that can be adopted by practitioners.Communication practices are context dependent.In the case of a distributed team, blended usage of rich-media communication tools, such as shared mind-map tools, videoconferencing, and promoting the exchange of team members between teams, is beneficial.In conclusion, communication can be expensive if teams do not apply the right strategies.Future research direction is to understand how to maximize product quality while reducing communication cost and how to identify the most beneficial communication strategy for the different stages of ASD.

[Context]: Communication plays an important role in any development process. However, communication overhead has been rarely compared among development processes. [Objective]: The goal of this work is to compare the communication overhead and the different channels applied in three agile processes (XP, Scrum, Scrum with Kanban) and in an unstructured process. [Method]: We designed an empirical study asking four teams to develop the same application with the four development processes, and we compare the communication overhead among them. [Results]: As expected, face-to-face communication is most frequently employed in the teams. Scrum with Kanban turned out to be the process that requires the least communication. Unexpectedly, despite requiring much more time to develop the same application, the unstructured process required comparable communication overhead (25% of the total development time) as the agile processes.

Context: Eliciting requirements from customers is a complex task. In Agile processes, the customer talks directly with the development team and often reports requirements in an unstructured way. The requirements elicitation process is up to the developers, who split it into user stories by means of different techniques. Objective: We aim to compare the requirements decomposition process of an unstructured process and three Agile processes, namely XP, Scrum, and Scrum with Kanban. Method: We conducted a multiple case study with a replication design, based on the project idea of an entrepreneur, a designer with no experience in software development. Four teams developed the project independently, using four different development processes. The requirements were elicited by the teams from the entrepreneur, who acted as product owner and was available to talk with the four groups during the project. Results: The teams decomposed the requirements using different techniques, based on the selected development process. Conclusion: Scrum with Kanban and XP resulted in the most effective processes from different points of view. Unexpectedly, decomposition techniques commonly adopted in traditional processes are still used in Agile processes, which may reduce project agility and performance. Therefore, we believe that decomposition techniques need to be addressed to a greater extent, both from the practitioners’ and the research points of view.

Abstract Traditional portfolio management tools and methods are challenging for software companies that use agile and lean approaches, because of the complex pre‐planning activities required. In this paper, traditional portfolio management tools and methods that have been conventionally used in industry and presented in literature are reviewed. Findings concerning their use in agile and lean software companies are investigated, on the basis of the literature review and semi‐structured interviews conducted in 7 agile and lean software companies in Finland. The findings indicate that the studied companies do not use traditional portfolio management tools and methods in their entirety. The software companies adapted suitable components from traditional portfolio management tools and methods into their practices. They also apply new tools and methods to manage their portfolios of offerings, to cope with the dynamics required by agile and lean methods. The companies' emphasis is on using tools to facilitate immediate feedback from customers, to assess the value of offerings in their portfolio. The results showed a growing interest in using Kanban at the portfolio level to provide a visual overview of offerings.

Coordination in software projects is a challenge, particularly when it comes to distributed software development (DSD). Agile software development is a well-known paradigm for improving software development; however, there is little understanding of its impact on DSD projects. This paper describes an empirical study conducted within two Software Factory settings in Finland and Italy to investigate how Scrumban can impact coordination in geographically distributed software development. It provides the results from a project case that applied Scrumban to develop a web-based application for Time-banking. This study shows that aspects of Scrumban, such as iterative development, enhanced visibility, and limited work-in-progress, would alleviate the challenges of leveraging resources, synchronization between distributed sites, communication, and culture. It also explains that technical and security issues in the coordination of distributed projects may demand for solutions other than Scrumban.

In the last decade, Kanban has been promoted as a means for bringing visibility to work while improving the software development flow, team communication and collaboration. However, little empirical evidence exists regarding Kanban use in the software industry. This paper aims to investigate the factors that users perceive to be important for Kanban use. We conducted a survey in 2015 among Kanban practitioners in the LeanKanban LinkedIn community. The survey results consist of 146 responses from 27 different organisations, with all respondents being experienced in using Kanban. The results show that practitioners perceived Kanban as easy to learn and useful in individual and team work. They also consider organisational support and social influence to be important determinants for Kanban use. Respondents noted various perceived benefits for using Kanban, such as bringing visibility to work, helping to reduce work in progress, improving development flow, increasing team communication and facilitating coordination. Despite the benefits, participants also identified challenges to using Kanban, such as organisational support and culture, difficulties in Kanban implementation, lack of training and misunderstanding of key concepts. The paper summarises the results and includes a discussion of implications for effective deployment of Kanban before describing future research needs.

In the last two decades, Agile and Lean approaches have gained wide acceptance in the software industry. In this realm, Kanban emerged in 2004 with a strong practitioner-driven support movement and today, Kanban is increasingly adopted to complement Scrum and other Agile methods. Kanban tends to focus on fast production, rapid and continual user feedback and interaction. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Industry needs graduates from universities having knowledge and skills to tackle the practical issues of real life software development. To facilitate software engineering students and fulfill industry need, the Department of Information Processing Science, University of Oulu, Finland, built a Software Factory laboratory (SWF) in 2012 based on Lean concept. This study examines factors in the SWF learning environment that affect learning of a SWF course by the students. It employs amended Computer laboratory Environment Inventory (CLEI) and Attitude towards Computers and Computing Courses Questionnaire instrument (ACCC) with two additional constructs: 1) Kanban board 2) Collaborative learning. The general findings indicate that SWF learning environment, collaborative learning and Kanban board play important role in software engineering students learning, academic achievements and professional skills gaining. The findings are helpful to develop a better understanding about learning environments. The information gathered in this study can also be used to improve the software engineering learning environment.