Context: Despite the popularity of Agile software development, achieving consistent project success remains challenging. Furthermore, identifying what contributes to project success is inconsistent across studies. In addition measuring success is incoherent, and existing SLR's are not systematic, explicit, and comprehensive making it hard to replicate investigations. Objective: This systematic literature review (SLR) identifies critical success factors (CSFs) in Agile projects to improve outcomes. Method: We analyzed 53 primary studies published between 2001 and 2024, employing thematic synthesis with content analysis to uncover recurring themes and patterns. Results: Our analysis yielded 21 CSFs categorized into five themes: organizational, people, technical, process, and project. These factors range from management dedication and organizational environment to team effectiveness, emotional mindset, and Agile practices. Team effectiveness and project management emerged as the most frequently cited CSFs, highlighting the importance of people and process factors to Agile project outcomes. Conclusions: These interpreted and integrated themes and factors contributed to developing a theoretical framework to identify how these factors contribute to project success. This study offers valuable insights for researchers and practitioners, guiding future research to validate these findings and develop targeted interventions to enhance Agile project success.
Context: LLM-based Text-to-SQL has advanced quickly, but benchmark and training datasets may contain defects that distort evaluation and fine-tuning. Prior audits remain fragmented, addressing dimensions in isolation. Objective: This paper proposes text2sql-dataset-analyzer, an open-source framework that audits Text-to-SQL datasets across five complementary quality dimensions within a single reproducible pipeline. Method: The framework covers five dimensions: database schema integrity, SQL syntactic structure and complexity, execution testing, antipattern detection, and semantic correspondence. Semantic correspondence is evaluated via an LLM-as-a-judge committee with majority voting. An analytical database stores the resulting metrics for direct querying and Markdown report generation, while structured JSONL output records per-item annotations. Results: Auditing all 11,840 Spider 1.0 examples reveals quality issues despite 99.97% of queries passing execution checks. Schema and data checks identify 51 structural foreign-key errors and 41,927 row-level referential-integrity violations, 41,913 of them in just three of 206 databases. At the item level, the audit flags unanimously Incorrect (8-10%), disputed (23-27%), and Unanswerable NL-SQL pairs, together with SQL antipatterns associated with correctness, robustness, and portability concerns. Manual review of 367 flagged items confirms genuine defects in 263, including 26 consensus Unanswerable questions and all 17 Cartesian-product join bugs. Conclusions: Multi-dimensional validation exposes dataset defects missed by executability-based checks. We release the open-source framework and a prioritized remediation roadmap for a popular Text-to-SQL benchmark. The downstream impact of these defects on model training and benchmark scores remains future work.
Context: The post-ChatGPT surge has rapidly reframed Information Systems (IS) research and practice. As organizations and society grapple with Generative AI (GenAI) adoption, a body of secondary studies and research agendas has emerged to synthesize early evidence and chart directions for future inquiry. Objective: This study conducts a systematic literature review of secondary studies and research agenda/roadmap papers to synthesize the state of knowledge on GenAI's benefits and challenges in IS, and to identify future research directions. Method: We performed a systematic search across Scopus, Web of Science, and the AIS eLibrary for publications from 2023 onwards. Following a rigorous, multi-stage screening process, we selected a final set of 28 papers (18 secondary studies and 10 research agendas) for analysis using bibliometric mapping and thematic analysis. We also conducted a quality assessment of all sources to gauge confidence in each source's contribution to the findings. Results: GenAI offers transformative potential to drive productivity, accelerate innovation, personalize services, and democratize access to expertise. However, its adoption is constrained by interrelated challenges: technical unreliability (hallucinations, performance drift), societal-ethical risks (bias, malicious misuse, skill erosion), and a governance vacuum (privacy, accountability, intellectual property). Conclusions: Interpreted through a socio-technical lens, our findings reveal a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization. To bridge this gap, we propose a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions -- emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance. For practitioners and policymakers, responsible adoption requires balancing automation with human augmentation alongside transparent governance and adaptive regulations to ensure broadly shared benefits.
Context: Large-scale agile (LSA) is inherently characterized by socio-technical complexities (e.g., system dependencies, extensive cross-team coordination, and distributed organizational structures). Onboarding of newcomers is a critical challenge in LSA environments and it remains underexplored compared to small-scale agile settings. Objective: Our study aims to investigate the onboarding processes and challenges within LSA projects. Method: In this exploratory qualitative study, we conducted 41 semi-structured interviews across four Swedish software companies. Results: We identified onboarding issues that result in negative outcomes, such as cognitive overload, psychological safety constraints, and cross-team collaboration hurdles. These factors create steep learning curves and knowledge deficiencies that contribute to long-term socio-technical debt. Conclusions: We provide actionable recommendations for practitioners to improve their onboarding processes. In particular, we recommend designing competency-based rather than time-based onboarding plans, expanding social integration through structured cross-team exposure, and addressing the gap between textbook agile and real-world LSA practices through explicit expectation setting.
Context: Software requirements outline customer expectations for their software and are critical for successful project outcomes. As software becomes increasingly complex due to its size and diverse features, it is vital to prioritize these requirements to utilize development resources effectively. To address this challenge, researchers are exploring new strategies and improved solutions using artificial intelligence (AI) tools. In our systematic literature review conducted in 2023, we found that existing requirements prioritization techniques predominantly rely on human input and have several limitations. These include inaccuracies, overlapping results, scalability issues, and excessive time consumption. These challenges can be mitigated by incorporating key features from AI-based software requirements prioritization techniques. Objective: The primary goal of this study is to develop a Hybrid AI-based requirements prioritization technique named as “KPSO-Fuzzy that effectively balances user preferences and technical dependencies. Method: We propose a Hybrid prioritization method called Hybrid KPSO-Fuzzy. First, we use K-Means clustering to group technically dependent functional requirements into three distinct clusters. In the next phase, we apply the Particle Swarm Optimization (PSO) algorithm along with Fuzzy Logic to prioritize each cluster simultaneously. Clusters that achieve higher accuracy will be selected to create the most effective prioritized lists of requirements. We conducted extensive experiments to validate our proposed approach, focusing on accuracy, scalability, and computation time. Results: The comparative analysis shows that our proposed Hybrid KPSO-Fuzzy method outperforms PSO, Fuzzy Logic, Multi-objective Artificial Bee Colony optimization and Hybrid Genetic Algorithms in terms of accuracy, scalability, and efficiency. Conclusions: Overall, this study clarifies the application domains for various AI-based techniques, enabling users to maximize their benefits.
