Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable. 

Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

This paper explores the relationship between self-organisation andtailored roles in large agile software development teams. The casestudy examines a Swedish IT company that has introduced the roleof “Security Master” in Scrum teams. The teams are developing avery large and secure 5G solution. Twenty semi-structuredinterviews were conducted and deductively analysed. The resultsshed light on the Security Master role, its need, responsibilities andimpact on secure coding. The paper concludes with a discussion oflessons learned and recommendations for future research in thecontext of large, security-sensitive projects.

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.

Reflective learning diaries can help students develop self-regulated learning (SRL), a critical skill for success. Forteachers such learning diaries can be a valuable tool for formative evaluation of SRL. However, there is limitedresearch on the use of reflective diaries in software engineering and information systems education. To address thisgap, we conducted a case study in which reflective learning diaries were used in a course. The goal of this research isto investigate the potential of using reflective diaries to formatively evaluate three key features of SRL: conceptions ofknowledge, conceptions of learning, and strategies for monitoring and regulating learning. Our findings suggest thatreflective diaries is a valuable tool for formative evaluation of SRL in software engineering education. Reflection onlearning experiences helps students develop self-awareness of their SRL skills and identify areas for improvement.Instructors can use this information to provide students with targeted feedback and support. By using reflective diariesto promote dialogic feedback, instructors can help students to develop a deeper understanding of their own learningand to become more effective self-regulated learners. The study conclude with lesson learned that provide practicalrecommendations for educators who are swiftly moving towards online teaching.

The software companies are using Agile methods and practices totackle challenges in the rapidly changing environments and increasingly com-plex software systems. However, companies developing cyber physical systems(CPS) are still infancy in the use of Agile methods and hesitate to adopt. Thissystematic literature review was conducted in order to analyze the current trendsof Agile methods use for CPS development. The search strategy resulted in 101papers, of which 15 were identified as primary studies relevant to our research.The results show growing trend of Agile processes and Scrum is widely usedreported for CPS development. The primary studies also exhibits a growinginterest in teaching Agile in embedded systems, CPS and other engineeringdegree programs. The reported challenges included synchronization of softwareand hardware development, software and hardware developers use differentvocabulary, lack of visibility and track of software releases and project progress.Additionally, lesson learned were extracted from the primary studies for guidingthe practitioners interested in adopting Agile for CPS development.

In 2004, Kanban successfully entered the Agile and Lean realm. Since then, software companies have been increasingly using it in software development teams. The goal of this study is to perform an empirical investigation on antecedents considered as important for achieving optimum benefits of Kanban use and to discuss the practical implications of the findings. We conducted an online survey with software professionals from the Lean Software Development LinkedIn community to investigate the importance of antecedents of using Kanban for achieving optimum benefits. Our study reveals that subjective norm, organizational support, ease of use, Kanban use experience and training are the antecedents for achieving expected benefits of Kanban. The potential benefits of Kanban use can only be realized when the key antecedents are not only identified, but also infused across an organization. When managing the transition to or using Kanban, practitioners need to adapt their strategies on the extent of various antecedents, a few identified in this study.

This paper investigates factors affecting business analytics (BA) in software and systems development projects. This is the first study to examine business analytics continuance in projects from Pakistani software professional’s perspective. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data was analyzed using partial least squares structural equation modelling techniques. Our structural model is able to explain 40% variance of BA continuance intention, 62% variance of satisfaction, 69% variance of technological compatibility, and 59% variance of perceived usefulness. Technological compatibility and perceived usefulness are the significant factors that can affect BA continuance intention in software and systems projects. Surprisingly the results show that satisfaction does not affect BA continuance intention. 

Primal-dual interior-point method (PDIPM) is the most efficient technique for solving sparse linear programming (LP) problems. Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs) have the potential to meet this requirement. However, their peculiar architecture entails a positive relationship between problem density and speedup, conversely implying a limited affinity of GPUs for problem sparsity. To overcome this difficulty, the state-of-the-art hybrid (CPU-GPU) implementation of PDIPM exploits presence of supernodes in sparse matrices during factorization. Supernodes are groups of similar columns that can be treated as dense submatrices. Factorization method used in the state-of-the-art solver performs only selected operations related to large supernodes on GPU. This method is known to underutilize GPU’s computational power while increasing CPU-GPU communication overhead. These shortcomings encouraged us to adapt another factorization method, which processes sets of related supernodes on GPU, and introduce it to the PDIPM implementation of a popular open-source solver. Our adaptation enabled the factorization method to better mitigate the effects of round-off errors accumulated over multiple iterations of PDIPM. To augment performance gains, we also used an efficient CPU-based matrix multiplication method. When tested for a set of well-known sparse problems, the adapted solver showed average speed-ups of approximately 55X, 1.14X and 1.05X over the open-source solver’s original version, the state-of-the-art solver, and a highly optimized proprietary solver known as CPLEX, respectively. These results strongly indicate that our proposed hybrid approach can lead to significant performance gains for solving large sparse problems.

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.

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 semi-structured 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.

Providing opportunities for students to work on real-world software development projects for real customers is critical to prepare students for the IT industry. Such projects help students to understand what they will face in the industry and experience real customer interaction and challenges in collaborative work. To provide this opportunity in an academic environment and enhance the learning and multicultural teamwork experience, the University of Oulu, Finland offers the software factory (SWF) project. This paper presents the design of the SWF course and the learning environment and assessment techniques, and it discusses the importance of reflective learning diaries and serious games. Additionally, this paper examines factors in the SWF learning environment that affect student learning in the SWF course. Survey data were collected from the last six years of SWF projects. The results show that students consider the SWF to be a good collaborative learning environment that helps them achieve academic triumphs and enhances various professional skills. The learning diaries are effective for increasing students’ learning experiences as well as providing an opportunity for teaching staff to monitor students’ progress and offer better facilitation. These results are helpful for academic institutions and industry when developing such a learning environment.

Context: The software engineering researchers and practitioners echoed the needfor investigations to better understand the engineers developing software andservices. In light of current studies, there are significant associations between thepersonalities of software engineers and their work preferences. However, limitedstudies are using psychometric measurements in software engineering.Objective: We aim to evaluate attitudes of early-stage software engineers andinvestigate link between their personalities and work preferences.Method: We collected extensive psychometric data from 303 graduate-levelstudents in Computer Science programs at four Pakistani and one Swedish universityusing Five-Factor Model. The statistical analysis investigated associations betweenvarious personality traits and work preferences.Results: The data support the existence of two clusters of software engineers, one ofwhich is more highly rated across the board. Numerous correlations exist betweenpersonality 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 settimetable. Similar relationships between personalities and occupational choices arealso evident in the earlier studies. More neuroticism is reported in femalerespondents than in male respondents. Higher intelligence was demonstrated bythose who worked on the“entire development process”and“technical componentsof the project.”Conclusion: When assigning project tasks to software engineers, managers might usethe statistically significant relationships that emerged from the analysis of personalityattributes. It would be beneficial to construct effective teams by taking personalityfactors like extraversion and agreeableness into consideration. The study techniquesand analytical tools we use may identify subtle relationships and reflect distinctionsacross various groups and populations, making them valuable resources for bothfuture academic research and industrial practice.

To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics.

The development of software and systems is a complex task that involves social, technical and organisational factors. Technical debt is a well-known concept that refers to the negative consequences of taking shortcuts in software development. However, organisational debt (OD) is a less well-known phenomenon that arises due to shortcuts in the organisational structure and processes of a software organisation. The lack of a clearly defined OD makes it difficult to identify and manage this type of debt. This study presents a multi-vocabulary literature review that consolidates an understanding of the nature of OD and its impact on software organisations. OD encompasses a set of attributes, precedents and outcomes. In addition, this study highlights the five causes and mitigation strategies of OD. The results of this study indicate that software companies are facing a major issue with OD. Future research should include empirical studies to validate techniques that can assist software professionals in managing OD to address this issue.

The development of software and systems is a complex task that involves social, technical and organisational factors. Technical debt is a well-known concept that refers to the negative consequences of taking shortcuts in software development. However, organisational debt (OD) is a less well-known phenomenon that arises due to shortcuts in the organisational structure and processes of a software organisation. The lack of a clearly defined OD makes it difficult to identify and manage this type of debt. This study presents a multi-vocabulary literature review that consolidates an understanding of the nature of OD and its impact on software organisations. OD encompasses a set of attributes, precedents and outcomes. In addition, this study highlights the five causes and mitigation strategies of OD. The results of this study indicate that software companies are facing a major issue with OD. Future research should include empirical studies to validate techniques that can assist software professionals in managing OD to address this issue.

Psychological safety, a pivotal factor in team dynamics, has been proposed as a crucial determinant of success in agile software development (ASD) teams and learning. However, the extent of its influence within the domain of large-scale agile (LSA) software development teams remains underexplored. This research investigates the multifaceted dimensions of psychological safety within LSA teams, examining both its precursors and outcomes. This study conducted semi-structured interviews with software professionals actively involved in LSA projects within a Swedish software consultancy company. The findings underscore the intricate nature of establishing a psychologically safe environment within LSA teams, revealing it as a multidimensional construct necessitating a proactive leadership approach, fostering open communication, and cultivating an ecosystem of constructive feedback. The study highlights the critical importance of intentionally shaping teams to facilitate continuous learning, ensuring remuneration safety, and implementing a comprehensive onboarding process for incoming team members. By fostering psychologically safe settings, LSA teams enhanced teamwork dynamics, heightened job satisfaction, and facilitation continuous learning and development. Notably, the absence of such an environment exacerbates the phenomenon of brain drain, exposing the tangible consequences of overlooking this fundamental aspect of organizational culture. This study proposes avenues for future research directions, aiming to further unravel the nuances of psychological safety and its cascading effects within the realm of contemporary LSA software development context.

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.

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 self-organisation. 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 self-organisation.

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.

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.

The International Conference on Agile Software Development (XP) was established almost sixteen years ago. Based on data from Scopus database, a total of 789 papers have been published in between years of 2002 and 2018. We employed bibliometrics analysis and topic modeling with R/RStudio to analyze these published papers from various dimensions, including the most active authors, collaboration of authorship, most cited papers, used keywords and trends of probable topics from the titles and abstracts of those papers. The results show that the first five years of XP conference cover nearly 40% of the papers published until now and almost 62% of the XP papers have been 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 and problems faced by the practitioners in the industry. The results highlight the most influential researchers and institutions, and the collaboration between the authors in the conference papers. The approach applied in this study can be extended to other software engineering venues and can be applied to large-scale studies.

Optimization problems lie at the core of scientific and engineering endeavors. Solutions to these problems are often compute-intensive. To fulfill their compute-resource requirements, graphics processing unit (GPU) technology is considered a great opportunity. To this end, we focus on linear programming (LP) problem solving on GPUs using revised simplex method (RSM). This method has potentially GPU-friendly tasks, when applied to large dense problems. Basis update (BU) is one such task, which is performed in every iteration to update a matrix called basis-inverse matrix. The contribution of this paper is two-fold. Firstly, we experimentally analyzed the performance of existing GPU-based BU techniques. We discovered that the performance of a relatively old technique, in which each GPU thread computed one element of the basis-inverse matrix, could be significantly improved by introducing a vectorcopy operation to its implementation with a sophisticated programming framework. Second, we extended the adapted element-wise technique to develop a new BU technique by using three inexpensive vector operations. This allowed us to reduce the number of floating-point operations and conditional processing performed by GPU threads. A comparison of BU techniques implemented in double precision showed that our proposed technique achieved 17.4% and 13.3% average speed-up over its closest competitor for randomly generated and well-known sets of problems, respectively. Furthermore, the new technique successfully updated basisinverse matrix in relatively large problems, which the competitor was unable to update. These results strongly indicate that our proposed BU technique is not only efficient for dense RSM implementations but is also scalable.

Purpose: This paper seeks to examine how expectations from business analytics (BA) by members of agile information systems development (ISD) teams affect their perceptions and continuous use of BA in ISD projects. Design/methodology/approach: Data was collected from 153 respondents working in agile ISD projects and analysed using partial least squares structural equation modelling techniques (PLS-SEM). Findings: Perceived usefulness and technological compatibility are the most salient factors that affect BA continuance intention in agile ISD projects. The proposed model explains 48.4% of the variance for BA continuance intention, 50.6% of the variance in satisfaction, 36.7% of the variance in perceived usefulness and 31.9% of the variance in technological compatibility. Research limitations/implications: First, this study advances understanding of the factors that affect the continuous use of BA in agile ISD projects; second, it contextualizes the expectation-confirmation model by integrating technological compatibility in the context of agile ISD projects. Originality/value: This is the first study to investigate BA continuance intention from an employee perspective in the context of agile ISD projects.

Context: 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. Objective: 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). Method: 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. Results: 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. Conclusions: 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. 

This book constitutes the refereed proceedings of the S3E 2023 Topical Area, 24th Conference on Practical Aspects of and Solutions for Software Engineering, KKIO 2023, and 8th Workshop on Advances in Programming Languages, WAPL 2023, as Part of FedCSIS 2023, held in Warsaw, Poland, during September 17–20, 2023.

The 6 revised papers presented in this book were carefully reviewed and selected from a total of 55 submissions. They focus on new ideas and developments in practical aspects and solutions for software engineering.

Industry 4.0 has been identified as a major contributor to the era of digitalisation. Its implications for sustainable development have gained widespread attention from the perspectives of the triple bottom line, sustainable business models and circular economy. The purpose of this paper is to map the broad field of sustainable development and investigate the key research areas which comprises the aforementioned perspectives under Industry 4.0 framework. A systematic mapping review was conducted by searching five databases for relevant literature published between 1st January 2012 and 17th April 2020. The search yielded 4,291 papers of which 81 were identified as primary papers relevant to the research herein. The primary findings are that the majority of sustainability research focuses on conceptual analysis, and the Internet of Things is dominantly cited with an emphasis on achieving the triple bottom line benefits. Sustainable development in the Industry 4.0 context contributes to circular economic objectives by achieving social, economic, and environmental benefits. Triple bottom line studies mainly focus on Industry 4.0 adoption and implementation, sustainable supply chains, smart and sustainable cities, and smart factories. Circular economy and sustainable business models as emerging research themes that focus on Industry 4.0 adoption and implementation, as well as sustainable supply chains. Our analysis consolidates emerging research patterns areas in both the Industry 4.0 and sustainability literature. Furthermore, it identifies salient research gaps and suggests future research.

As the dyad of Industry 4.0 (I4.0) and innovation have gained greater attention from researchers, practitioners and policy makers, integration of sustainability and sustainable development paradigms to this dyad have become fundamental to sustain businesses' competitive advantage. A variety of I4.0 based innovations with several sustainability implications exists in the literature, but they largely address independent and distinct knowledge areas, which yields an opportunity to explore the interconnections of I4.0-innovation-sustainability nexus. Therefore, this research performs a systematic literature review to synthesize the nexus by investigating how a combination of I4.0 technologies and different types of innovations, could contribute to sustainable development thereby providing sustainability implications. Our review portfolio derived from three databases analyzed 58 journal articles that addressed the simultaneous links of I4.0-innovation-sustainability. The primary findings show that I4.0 results in various innovation types including process, product, business model, supply chain, organizational, open, and marketing innovations that advance triple bottom line (TBL) sustainability, circular economy (CE), sustainable business models (SBMs) and achievement of sustainable development goals (SDGs). While most studies focus on process, product, and business model innovations with TBL and CE implications, more research is required to address the significant but overlooked areas such as open, organizational, and marketing innovations to advance business model sustainability and SDGs.

Informal caregivers are a significant resource when elderly people need assistance and support to remain living at home. Today, state-of-the-art technology provides the possibilities of applying ways to ease the workload and make it possible to stay at home instead of living in an institutional care setting, such as hospitals or special sheltered accommodations. The current study analysed how information and communication technology (ICT) could support healthcare in Chinese homes from the perspectives of informal caregivers and healthcare professionals. The study focused on elderly people who benefit from caregiving or need personal assistance to help them live at home. A mapping study was conducted to identify existing ICT solutions, and qualitative semi-structured interviews were performed to obtain the perspectives of informal caregivers and professionals. The contributions were identified as objectives of using ICT solutions, relatives’ feedback on ICT solutions, opinions about popular ICT solutions and thoughts about future ICT solutions. The empirical study revealed that alarming, communication, monitoring, positioning and assistance are the most important reasons to acquire and apply ICT-based support for elderly people living at home.

Seismic activity prediction has been a challenging research domain: in this regard, accurate prediction using historical data is an intricate task. Numerous machine learning and traditional approaches have been presented lately for seismic activity prediction; however, no generalizable model exists. In this work, we consider seismic activity predication as a binary classification problem, and propose a deep neural network architecture for the classification problem, using historical data from Chile, Hindukush, and Southern California. After obtaining the data for the three regions, a data cleaning process was used, which was followed by a feature engineering step, to create multiple new features based on various seismic laws. Afterwards, the proposed model was trained on the data, for improved prediction of the seismic activity. The performance of the proposed model was evaluated and compared with extant techniques, such as random forest, support vector machine, and logistic regression. The proposed model achieved accuracy scores of 98.28%, 95.13%, and 99.29% on the Chile, Hindukush, and Southern California datasets, respectively, which were higher than the current benchmark model and classifiers. In addition, we also conducted out-sample testing, where the evaluation metrics confirmed the generality of our proposed approach.

Software development encompasses various factors beyond technical considerations. Neglecting non-technical elements like individuals, processes, culture, and social and organizational aspects can lead to debt-like characteristics that demand attention. Therefore, we introduce the non-technical debt (NTD) concept to encompass and explore these aspects. This indicates the applicability of the debt analogy to non-technical facets of software development. Technical debt (TD) and NTD share similarities and often arise from risky decision-making processes, impacting both software development professionals and software quality. Overlooking either type of debt can lead to significant implications for software development success. The current study conducts a comprehensive multivocal literature review (MLR) to explore the most recent research on NTD, its causes, and potential mitigation strategies. For analysis, we carefully selected 40 primary studies among 110 records published until October 1, 2022. The study investigates the factors contributing to the accumulation of NTD in software development and proposes strategies to alleviate the adverse effects associated with it. This MLR offers a contemporary overview and identifies prospects for further investigation, making a valuable contribution to the field. The findings of this research highlight that NTD's impacts extend beyond monetary aspects, setting it apart from TD. Furthermore, the findings reveal that rectifying NTD is more challenging than addressing TD, and its consequences contribute to the accumulation of TD. To avert software project failures, a comprehensive approach that addresses NTD and TD concurrently is crucial. Effective communication and coordination play a vital role in mitigating NTD, and the study proposes utilizing the 3C model as a recommended framework to tackle NTD concerns.

Background: Agile methodologies emphasise iterative development, customer collaboration, and flexibility in software development. However, challenges arise when agile practices are adoptedin larger projects. Process inefficiencies and redundancies, knownas process debt, result from the compounded complexities of expanding agile processes and workflows. However, strategies tounderstand and tackle it remain markedly inadequate.Aims: This study investigates process debt types, causes, andeffects in large-scale agile development and its connection withtechnical debt.Method: In this case study, we conducted fifteen semi-structuredinterviews with a Nordic IT company, primarily focusing on telecomrelated products like 5G secure solutions, testing tools, and basestation software. We performed a thematic analysis to examine thedata qualitatively.Results: The thematic analysis identified five process debt typeswith 28 sub-types: documentation (3), roles & responsibilities (5),synchronization (5), inefficiency & unsuitability (12), and infrastructure debt (3) identified causes and effects of process debt andidentified the correlation of process debt to technical debt based ondescriptions from interview data and researchers’ insights.Conclusions: Process debt, stemming from flawed agile practices in large-scale development, causes inefficiencies, reduces quality, and extends timelines, risking technical debt. Its managementis essential for the success of these projects

We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.

Earlier research has focused on technical debt (TD). While numerous issues connected to non-technical aspects of software development (SD) that are equally worthy of”debt” status are neglected. Simultaneously, these types of debts regularly develop significant challenges to be addressed, demonstrating that the debt metaphor may be used to reason about elements other than technical ones. It motivates us to create the new umbrella term”Non-Technical Debt” (NTD) to investigate people, processes, culture, social, and organizational concerns under its cover. All types of debt are similar in some ways, and they are often caused by making risky decisions. Therefore, ignoring any one dimension of debt can have severe consequences on the successful completion of SD projects. This study investigates recent literature on the current state of knowledge about NTD, its causes, and mitigation strategies. By using a thematic analysis approach, we found five NTD types (i.e., people, process, culture, social, and organizational). We further identified their accumulation causes and discussed remedies for mitigation. 

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.

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.

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.