Information and Software Technology最新文献

{"title":"Empirical analysis of generative AI tool adoption in software development","authors":"Deo Shao,&nbsp;Fredrick Ishengoma","doi":"10.1016/j.infsof.2026.108036","DOIUrl":"10.1016/j.infsof.2026.108036","url":null,"abstract":"<div><h3>Context</h3><div>Software development is evolving with the emergence of Generative AI (GAI) tools that boost productivity, reduce manual errors, and accelerate workflows. However, little is known about how users perceive the usability, effectiveness, and security of these tools, especially among varied user populations.</div></div><div><h3>Objectives</h3><div>This study examines the determinants of GAI tool adoption. Specifically, it examines the behavioural determinants driving GAI adoption in software development and investigates how students compare with professionals in their perception of GAI adoption.</div></div><div><h3>Methods</h3><div>This study employs a cross-sectional, quantitative approach, comprising structured surveys distributed to software engineering students and senior engineers. The survey was designed based on the UTAUT framework. Data was collected from 305 participants (125 students, 133 professional developers, and 47 other tech professionals; industry total = 180). Descriptive statistics, <em>t</em>-tests, and regression analysis were conducted to analyse data and report trends and predictors of adoption intention.</div></div><div><h3>Results</h3><div>Social influence was the most important predictor of adoption intention (<em>β</em> = 0.945, <em>p</em>&lt; 0.001), and its effect differed between groups. Compared to professionals, students are more cautious about security, though their responses are less technically specific. Professional developers employ systematic refinement strategies; a large percentage make extensive code changes to improve maintainability and ensure architectural alignment. By contrast, students exhibit different usage behaviour, focusing more on getting the final product working but less on code refinement and security issues.</div></div><div><h3>Conclusion</h3><div>This study fills the empirical gap in the diffusion of generative AI into software development. The findings suggest different patterns between students and professional developers. The results are of interest to educators, developers, and industry leaders. Future studies should examine adoption trends among a broader range of user groups and assess the long-term effects of GAI tools on software engineering.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108036"},"PeriodicalIF":4.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus planning boosts LLM code generation","authors":"Chao Wen ,&nbsp;Jie Liu ,&nbsp;Liang Du","doi":"10.1016/j.infsof.2026.108030","DOIUrl":"10.1016/j.infsof.2026.108030","url":null,"abstract":"<div><div>While large language models (LLMs) have demonstrated impressive ability in natural language processing (NLP), they are struggling for addressing the code generation tasks with complicated human intent. It is universally recognized that humans require insights into problem descriptions, elaborate plans from collaborative perspectives and consciously organize modules prior to coding implementation. To achieve this aim, we introduce consensus to boost multi-agent prompting approach to code generation tasks by imitating human developers. The insights into consensus among distinct candidate plans are leveraged by LLM agent for mitigating discrepancies. The discrepancies indicate overlooked crucial details that may lead to potential errors. Besides, the consensus plan is exploited to firstly construct code modules at distinct levels and then hierarchically organize them for final code generation. We conduct extensive experiments on eight program synthesis benchmarks, three of which are challenging problem-solving. Experimental results show that the proposed framework showcases the improved reflection on code generation, achieving new state-of-the-art (pass@1) results. Moreover, our approach consistently delivers superior performance across various programming languages and varying problem difficulties. Code available at <span><span>https://github.com/AISP-group/CPCG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108030"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wise recommender: LLMs refined by iterative critics","authors":"Zhisheng Yang ,&nbsp;Xiaofei Xu ,&nbsp;Ke Deng ,&nbsp;Li Li","doi":"10.1016/j.infsof.2026.108021","DOIUrl":"10.1016/j.infsof.2026.108021","url":null,"abstract":"<div><h3>Context:</h3><div>Large Language Models (LLMs) have been applied to recommendation tasks, giving rise to the new paradigm of LLM-as-Recommendation Systems (LLM-as-RS). Existing methods fall into two categories: tuning and non-tuning. While tuning strategies offer better task alignment, they are expensive and require specialized training. Non-tuning strategies are easier to deploy but often lack task-specific knowledge, limiting their effectiveness.</div></div><div><h3>Objective:</h3><div>This study aims to enhance the recommendation quality of non-tuning LLM-based systems by addressing their lack of task awareness.</div></div><div><h3>Method:</h3><div>We propose a novel approach, Critique-based LLMs as Recommendation Systems (Critic-LLM-RS), which introduces an independent machine learning model—the Recommendation Critic—to provide feedback on LLM-generated recommendations and guide the LLM toward improved recommendation strategies.</div></div><div><h3>Results:</h3><div>Experiments on multiple real-world datasets demonstrate that Critic-LLM-RS significantly outperforms existing non-tuning approaches, regardless of whether open-source or proprietary LLMs are used.</div></div><div><h3>Conclusion:</h3><div>Critic-LLM-RS enhances the task adaptability of non-tuning LLMs through a collaborative feedback mechanism, offering a new solution for building efficient and easily deployable recommendation systems.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108021"},"PeriodicalIF":4.3,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User-centric requirements prioritization in mHealth applications: Insights from a Discrete Choice Experiment","authors":"Wei Wang ,&nbsp;Hourieh Khalajzadeh ,&nbsp;John Grundy ,&nbsp;Anuradha Madugalla ,&nbsp;Humphrey O. Obie","doi":"10.1016/j.infsof.2026.108014","DOIUrl":"10.1016/j.infsof.2026.108014","url":null,"abstract":"<div><h3>Context:</h3><div>Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a promising approach to personalizing interactions and improving user experience. However, their adoption remains limited, partly due to a lack of understanding of how users perceive and evaluate different adaptation strategies. Addressing this gap is crucial for advancing user-centered design and requirements engineering in software systems for health contexts.</div></div><div><h3>Objective:</h3><div>This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design.</div></div><div><h3>Method:</h3><div>A Discrete Choice Experiment (DCE) was conducted with 186 participants living with chronic conditions who regularly use mHealth applications. Each participant completed a series of choice tasks, selecting their preferred adaptation designs from scenarios composed of six attributes with varying levels. A mixed logit model was applied to examine preference heterogeneity. Subgroup analyses were also conducted to explore variations in preferences across age, gender, health condition, and coping mechanism.</div></div><div><h3>Results:</h3><div>Participants preferred adaptation designs that preserved usability, offered controllability, introduced changes infrequently, and applied small-scale modifications. Conversely, adaptations affecting frequently used functions and those involving caregiver input were generally viewed less favorably. These findings highlight key trade-offs that influence user acceptance of adaptive mHealth interfaces.</div></div><div><h3>Conclusion:</h3><div>This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. These insights provide actionable guidance for designing more user-centered adaptive interfaces and contribute to advancing requirements prioritization practices in software engineering—particularly in the context of health technologies.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108014"},"PeriodicalIF":4.3,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Requirements-driven analysis of variability in configurable software","authors":"Chin Khor,&nbsp;Robyn R. Lutz","doi":"10.1016/j.infsof.2026.108017","DOIUrl":"10.1016/j.infsof.2026.108017","url":null,"abstract":"<div><h3>Context:</h3><div>It is difficult, time-consuming, and error-prone to detect misalignments between the variability requirements in configurable software and the source code intended to implement those requirements.</div></div><div><h3>Objective:</h3><div>The paper reports progress in checking the consistency between variability requirements and their implementation.</div></div><div><h3>Method:</h3><div>To automate the consistency checking of variability requirements and variability source code, we create a variability model of configurable features and constraints from the requirements specification. We evaluate the consistency of the variability model against a formal representation of the presence conditions controlling variability in the source code. We generate a traceability-rich consistency dashboard for the developer of any misalignments and a minimal set of configurations providing full variability code coverage for variability testing. The approach is implemented in an open-source prototype tool called VarCHEK.</div></div><div><h3>Results:</h3><div>VarCHEK was evaluated on three diverse, configurable software projects. VarCHEK accurately identified variability requirements not implemented in the source code, found variabilities in the source code not specified in the requirements, and provided more relevant information to the user for troubleshooting and resolving inconsistencies than is currently available.</div></div><div><h3>Conclusion:</h3><div>This paper describes a new, practical way to automatically identify inconsistencies between the variability requirements specified for configurable software and the source code developed to implement those requirements.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108017"},"PeriodicalIF":4.3,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Android malware detection by using graph optimization of static features based on pre-trained language models","authors":"Nghi Hoang Khoa,&nbsp;Doan Minh Trung,&nbsp;Duong The Dat,&nbsp;Phan The Duy,&nbsp;Van-Hau Pham,&nbsp;Nguyen Tan Cam","doi":"10.1016/j.infsof.2026.108012","DOIUrl":"10.1016/j.infsof.2026.108012","url":null,"abstract":"<div><h3>Context:</h3><div>The Android platform is the dominant mobile operating system, making it a prime target for malware attacks. The increasing complexity of Android malware necessitates advanced detection methods that integrate modern machine learning techniques with security analysis.</div></div><div><h3>Objectives:</h3><div>This study aims to enhance Android malware detection and classification by leveraging pre-trained language models (PLMs) alongside graph learning techniques. The primary objective is to address challenges in transforming graph-based data into a format compatible with PLMs while preserving essential relational information.</div></div><div><h3>Methods:</h3><div>We propose <em>APSDroid</em>, a novel approach that combines permissions-intents (PIs) and API call graphs (ACGs) for Android malware analysis. APSDroid explores two distinct fusion strategies: raw-data-level fusion and feature-level fusion (including concatenation, self-attention, and cross-attention mechanisms) to evaluate their effectiveness in enriching semantic representations and improving detection robustness. The approach also incorporates forensic analysis to extract meaningful behavioral patterns and graph optimization techniques based on community detection and centrality measures to reduce complexity while retaining contextual flow.</div></div><div><h3>Results:</h3><div>Experiments conducted on the <em>CICMalDroid2020</em> dataset demonstrate the effectiveness of APSDroid. The model achieved an accuracy of <strong>97.40%</strong> in malware detection and <strong>94.23%</strong> in malware category classification. Furthermore, APSDroid with attention-based fusion mechanisms (e.g., self-attention and cross-attention) remained highly robust against obfuscation techniques, achieving <strong>98.69%</strong> F1-score in binary classification and <strong>83.98%</strong> in multi-class classification, outperforming several SOTA methods.</div></div><div><h3>Conclusion:</h3><div>APSDroid provides a robust and scalable solution for Android malware detection by integrating PLMs with optimized graph-based representations. This approach enhances malware analysis while addressing key challenges related to computational efficiency and relational data preservation. Future research will focus on improving scalability and extending APSDroid to detect emerging malware variants.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108012"},"PeriodicalIF":4.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BADS: A backdoor attack against code intent summarization engines","authors":"Yubin Qu ,&nbsp;Binyong Li ,&nbsp;Song Huang ,&nbsp;Peng Nie ,&nbsp;Long Li ,&nbsp;Yongming Yao","doi":"10.1016/j.infsof.2026.108018","DOIUrl":"10.1016/j.infsof.2026.108018","url":null,"abstract":"<div><h3>Context:</h3><div>Code Large Language Models (LLMs) have transformed software engineering practices by automating code understanding and documentation generation. However, relying on third-party training datasets and pre-trained models introduces significant security vulnerabilities. This is particularly critical in code intent summarization, where security implications must be accurately identified and communicated.</div></div><div><h3>Objective:</h3><div>This study investigates backdoor attacks targeting code intent summarization in LLMs, with particular focus on security-related code documentation. Our work addresses a critical gap in existing literature by proposing a novel structure-based backdoor attack method that exploits abstract syntax tree features as triggers, effectively manipulating model outputs while maintaining normal behavior for benign inputs.</div></div><div><h3>Methods:</h3><div>We designed a two-stage approach comprising trigger design and model fine-tuning. Our method first conducts a statistical analysis of abstract syntax tree depths in source code, then selects appropriate samples for poisoning based on their structural characteristics. We employed parameter-efficient fine-tuning techniques (LoRA) to embed these backdoors into code LLMs, and developed comprehensive evaluation metrics based on the similarity between security and functional intent summaries to assess attack effectiveness.</div></div><div><h3>Results:</h3><div>Our findings demonstrate that the proposed BADS method achieves superior attack success rates compared to baseline approaches, maintaining high effectiveness (92.41% ASR) even against state-of-the-art defense strategies. While traditional grammar-based triggers (PCFG) show advantages at lower poisoning ratios, our structure-based approach exhibits remarkable performance at higher ratios, highlighting a critical vulnerability in current code intent summarization systems.</div></div><div><h3>Conclusions:</h3><div>By focusing on structure-based backdoor attacks in code intent summarization, this study provides unique insights into securing code LLMs against supply chain attacks. Our work reveals how seemingly benign structural patterns can be exploited to manipulate security-critical documentation, laying the foundation for future research on developing more robust defense mechanisms against stealthy backdoor attacks in software engineering applications.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108018"},"PeriodicalIF":4.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FlowRepair: Search-based automated program repair of CPS controllers modeled in Simulink-Stateflow","authors":"Aitor Arrieta ,&nbsp;Pablo Valle ,&nbsp;Shaukat Ali","doi":"10.1016/j.infsof.2025.108010","DOIUrl":"10.1016/j.infsof.2025.108010","url":null,"abstract":"<div><h3>Context:</h3><div>Stateflow models are widely used in the industry to model the high-level control logic of Cyber–Physical Systems (CPSs) in Simulink. Many approaches exist to test Simulink models, but once a fault is detected, the process to repair it remains manual. Such a manual process increases the software development cost. Automated Program Repair (APR) techniques can significantly reduce this cost by automatically generating patches that fix bugs. However, current approaches face scalability issues to be applicable in the CPS context.</div></div><div><h3>Objectives:</h3><div>The goal of this paper is to propose an APR method which is scalable for Stateflow models.</div></div><div><h3>Method:</h3><div>We propose an automated search-based approach called <span>FlowRepair</span>, explicitly designed to repair Stateflow models. The novelty of <span>FlowRepair</span> includes, (1) a new algorithm that combines global and local search for patch generation; (2) a definition of novel repair objectives specifically tailored for repairing CPSs; (3) a set of mutation operators to repair Stateflow models automatically; and (4) an evaluation on a new dataset encompassing 19 faulty stateflow models with real bugs.</div></div><div><h3>Results:</h3><div>Our results suggest that (1) <span>FlowRepair</span> can fix bugs in stateflow models; (2) <span>FlowRepair</span> surpasses or performs similarly to a baseline APR technique inspired by a well-known CPS program repair approach.</div></div><div><h3>Conclusion:</h3><div>This paper presents the first tool for APR CPSs whose high-level control program is developed in Simulink-Staflow. The results show that the approach is effective and scalable to such complex systems.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108010"},"PeriodicalIF":4.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artifact validity under varying agent configurations in LLM-assisted software development: A comparative analysis","authors":"Dae-Kyoo Kim","doi":"10.1016/j.infsof.2026.108022","DOIUrl":"10.1016/j.infsof.2026.108022","url":null,"abstract":"<div><h3>Context:</h3><div>The integration of large language models (LLMs) into software engineering has advanced toward agent-based automation across the development lifecycle. However, the comparative effectiveness of different multi-agent orchestration strategies remains underexplored.</div></div><div><h3>Objective:</h3><div>This study examines how three agent configuration strategies – Task-Specialized (TS), Phase-Specialized (PS), and Process-Generalist (PG) – impact the validity of software artifacts generated across key development tasks.</div></div><div><h3>Methods:</h3><div>Using a unified LLM backend within a structured orchestration framework, we evaluate the three configurations across nine core software engineering tasks – covering requirements analysis, design modeling, implementation, and testing – within three application domains: Tour Reservation System (TORS), Smart Wallet System (SWS), and Food Order and Delivery System (FODS). Artifact validity is measured using structural and semantic criteria.</div></div><div><h3>Result:</h3><div>No configuration consistently outperforms the others across all tasks. The overall average validity score is 0.56, with zero standard deviation, indicating uniformly constrained performance. Validity is highest in early requirements tasks (0.63–0.85), moderate in implementation and testing (0.61), and lowest in modeling tasks (0.25–0.42). TS agents perform well in modeling tasks due to focused specialization; PS agents benefit from contextual continuity in tasks like operation identification and test design, though performance varies; PG agents offer stable but less tailored performance across the pipeline. All configurations perform best in the TORS domain, which features simple and modular requirements.</div></div><div><h3>Conclusions:</h3><div>Artifact quality appears more influenced by the LLM’s capabilities than orchestration strategy alone. However, task- and domain-specific variations suggest that adaptive or hybrid orchestration strategies – tailored to both task type and domain context – can enhance the effectiveness of agent-assisted software development. These findings support the need for more targeted specialization strategies and possibly domain-adapted LLMs.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"192 ","pages":"Article 108022"},"PeriodicalIF":4.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond platforms — Growing distributed transaction networks for digital commerce","authors":"Yvonne Dittrich ,&nbsp;Kim Peiter Jørgensen ,&nbsp;Ravi Prakash ,&nbsp;Willard Rafnsson ,&nbsp;Jonas Kastberg Hinrichsen","doi":"10.1016/j.infsof.2026.108016","DOIUrl":"10.1016/j.infsof.2026.108016","url":null,"abstract":"<div><h3>Context</h3><div>We talk of the internet as digital infrastructure; but we leave the building of digital 'rails' and 'roads' to the quasi-monopolistic platform providers that benefit from both vendor and customer log-in. Decentralised architectures provide a number of advantages: They are potentially more inclusive for small players; more resilient against adversarial events, and seem to generate more innovation. However, it is not well understood how to evolve, adapt and govern decentralised infrastructures.</div></div><div><h3>Objective</h3><div>This article reports empirical qualitative research on the development and governance of the Beckn Protocol, an open source protocol for decentralised transactions, the successful development of domain-specific adaptations, and implementation and scaling of commercial infrastructures based on it. It explores how the architecture and governance support local innovation for specific business domains, and how the domain-specific innovations feed back into the development of the core concept.</div></div><div><h3>Method</h3><div>The Beckn Protocol is researched as a defining element of a software ecosystem underpinning infrastructures for digital commerce. The research applied a case study approach, triangulating interviews with core members of the Beckn community with interviews with community leaders of domain specific adaptations and analysis of online documents and the protocol itself.</div></div><div><h3>Results</h3><div>The article shows the possibility of a decentralised approach to IT infrastructures. It analyses the Beckn Protocol, domain specific adaptations, and networks built on them with respect to architecture and evolution, community and governance, the outcome, and communication and collaboration. Based on the analysis, a number of generative mechanisms - socio-technical arrangements that support adoption, innovation, and scaling of infrastructures are highlighted.</div></div><div><h3>Conclusion</h3><div>The article discusses the importance of governance also concerning security of decentralised networks. It emphasises the importance of feedback loops to both provide input for technical evolution and to recognise misconduct and develop means to address it. Implications for practice and research are highlighted.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"193 ","pages":"Article 108016"},"PeriodicalIF":4.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}