{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:21:57Z","timestamp":1783513317688,"version":"3.55.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008331","name":"Universidade Federal Do Rio De Janeiro","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008331","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Software Qual J"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Software quality is undergoing a profound transformation, driven by state-of-the-art research on the application of emerging technologies in software development processes. Specifically, the use of generative Artificial Intelligence (AI) may represent an opportunity to advance the state of practice in this domain. This study aims to assess the industrial readiness and availability of Generative AI-based solutions for software quality, classifying them according to ISO\/IEC 25010 attributes and SDLC phases. An empirical assessment of the state of practice was conducted, employing a Rapid Multivocal Literature Review (RMLR) protocol as a data collection instrument to screen evidence from academic databases (Scopus) and grey literature (Google, GitHub, PapersWithCode). We identified 24 potentially usable solutions. However, the analysis reveals a low technological maturity, with most solutions being academic prototypes hampered by fundamental technical limitations and adoption challenges. These include the \u201clast mile problem\u201d in translating research prototypes into reliable, production-ready tools; the \u201cstrategic adoption dilemma\u201d forcing practitioners to trade off between proprietary lock-in and high open-source infrastructure costs; and the \u201cscarcity of realistic public data,\u201d which drives a generalization gap due to reliance on synthetic or leaked benchmarks. Generative AI in software quality remains an emerging but immature field, hampered by a critical reliability gap between academic prototypes and industrial needs. Advancing this domain requires moving beyond a narrow code-centric focus to address the quality of the AI systems themselves, expanding research across all SDLC phases and ISO 25010 attributes. We conclude with a roadmap advocating for contamination-free benchmarks, explainable architectures, and robust guidelines for real-world integration.<\/jats:p>","DOI":"10.1007\/s11219-026-09754-7","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:12:45Z","timestamp":1778663565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative AI solutions for software quality: Assessing industrial readiness"],"prefix":"10.1007","volume":"34","author":[{"given":"Andre","family":"Gheventer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia","family":"do Amaral Gurgel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Henrique Brito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Maiani de Mello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina","family":"Rocha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo","family":"Feitosa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilherme","family":"Horta Travassos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"issue":"1","key":"9754_CR1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11219-025-09731-6","volume":"33","author":"Z Ahmad","year":"2025","unstructured":"Ahmad, Z., & Kuo, M. M. Y. (2025). Improving story points estimation using ensemble machine learning. Software Quality Journal, 33(1), 35. https:\/\/doi.org\/10.1007\/s11219-025-09731-6","journal-title":"Software Quality Journal"},{"key":"9754_CR2","unstructured":"Ahmed, M. B. U., Harzevili, N. S., Shin, J., Pham, H. V., & Wang, S. (2025). SecVulEval: Benchmarking LLMs for Real-World C\/C++ Vulnerability Detection. Preprint at arxiv:2505.19828"},{"key":"9754_CR3","doi-asserted-by":"publisher","first-page":"112531","DOI":"10.1016\/j.jss.2025.112531","volume":"230","author":"S Alagarsamy","year":"2025","unstructured":"Alagarsamy, S., Tantithamthavorn, C., Takerngsaksiri, W., Arora, C., & Aleti, A. (2025). Enhancing large language models for text-to-testcase generation. Journal of Systems and Software, 230, 112531. https:\/\/doi.org\/10.1016\/j.jss.2025.112531","journal-title":"Journal of Systems and Software"},{"key":"9754_CR4","unstructured":"Baldassarre, M. T., Caivano, D., Dimauro, G., Romano, S., & Scanniello, G. (2021). On internet-of-things devices in ambient assisted living solutions. In International Conference on Information Systems Development."},{"key":"9754_CR5","doi-asserted-by":"publisher","first-page":"107751","DOI":"10.1016\/j.infsof.2025.107751","volume":"183","author":"L Banh","year":"2025","unstructured":"Banh, L., Holldack, F., & Strobel, G. (2025). Copiloting the future: How generative ai transforms software engineering. Information and Software Technology, 183, 107751. https:\/\/doi.org\/10.1016\/j.infsof.2025.107751","journal-title":"Information and Software Technology"},{"key":"9754_CR6","doi-asserted-by":"publisher","unstructured":"Bazzan, T., Olojo, B., Majda, P., Kelly, T., Yilmaz, M., Marks, G., & Clarke, P. (2024). Analysing the role of generative AI in software engineering - results from an MLR. In Systems, Software and Services Process Improvement. EuroSPI 2024 (pp. 163\u2013180). https:\/\/doi.org\/10.1007\/978-3-031-71139-8_11","DOI":"10.1007\/978-3-031-71139-8_11"},{"key":"9754_CR7","doi-asserted-by":"publisher","first-page":"107786","DOI":"10.1016\/j.infsof.2025.107786","volume":"185","author":"G Bhandari","year":"2025","unstructured":"Bhandari, G., Gavric, N., & Shalaginov, A. (2025). Generating vulnerability security fixes with code language models. Information and Software Technology, 185, 107786. https:\/\/doi.org\/10.1016\/j.infsof.2025.107786","journal-title":"Information and Software Technology"},{"key":"9754_CR8","doi-asserted-by":"crossref","unstructured":"Cartaxo, B., Pinto, G., & Soares, S. (2018). The role of rapid reviews in supporting decision-making in software engineering practice. In Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering 2018 (EASE \u201918) (pp. 24\u201334). Association for Computing Machinery (ACM), Christchurch, New Zealand. 10.1145\/3210459.3210462","DOI":"10.1145\/3210459.3210462"},{"key":"9754_CR9","unstructured":"Chen, X., Lin, M., Sch\u00e4rli, N., & Zhou, D. (2024). Teaching large language models to self-debug. In 12th International Conference on Learning Representations (ICLR 2024)."},{"key":"9754_CR10","doi-asserted-by":"publisher","unstructured":"Cruzes, D. S.,& Dyba, T. (2011). Recommended steps for thematic synthesis in software engineering. In 2011 International Symposium on Empirical Software Engineering and Measurement (pp. 275\u2013284). IEEE, Banff, Canada. https:\/\/doi.org\/10.1109\/ESEM.2011.36","DOI":"10.1109\/ESEM.2011.36"},{"issue":"1","key":"9754_CR11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/3631537","volume":"67","author":"M Cusumano","year":"2023","unstructured":"Cusumano, M. (2023). Nvidia at the center of the generative ai ecosystem\u2013for now. Communications of the ACM, 67(1), 33\u201335.","journal-title":"Communications of the ACM"},{"key":"9754_CR12","doi-asserted-by":"publisher","unstructured":"De\u00a0Vito, G., Lambiase, S., Palomba, F., & Ferrucci, F. (2023). Meet C4SE: Your new collaborator for software engineering tasks. In 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 235\u2013238). IEEE, Durres, Albania. https:\/\/doi.org\/10.1109\/SEAA60479.2023.00044","DOI":"10.1109\/SEAA60479.2023.00044"},{"key":"9754_CR13","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1109\/TSE.2024.3428972","volume":"50","author":"S Fakhoury","year":"2024","unstructured":"Fakhoury, S., Naik, A., Sakkas, G., Chakraborty, S., & Lahiri, S. (2024). Llm-based test-driven interactive code generation: User study and empirical evaluation. IEEE Transactions on Software Engineering, 50, 2254\u20132268. https:\/\/doi.org\/10.1109\/TSE.2024.3428972","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9754_CR14","doi-asserted-by":"publisher","unstructured":"Fan, J., Li, Y., Wang, S., & Nguyen, T. N. (2020). A c\/c++ code vulnerability dataset with code changes and cve summaries. In Proceedings of the 17th International Conference on Mining Software Repositories (MSR \u201920) (pp. 508\u2013512). ACM. https:\/\/doi.org\/10.1145\/3379597.3387501","DOI":"10.1145\/3379597.3387501"},{"key":"9754_CR15","doi-asserted-by":"publisher","unstructured":"Garousi, V., Felderer, M., M\u00e4ntyl\u00e4, M. V., & Rainer, A. (2020). Benefitting from the grey literature in software engineering research. In M. Felderer, & G. H. Travassos (Eds.), Contemporary Empirical Methods in Software Engineering (pp. 385\u2013413). Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-32489-6_14","DOI":"10.1007\/978-3-030-32489-6_14"},{"key":"9754_CR16","doi-asserted-by":"publisher","unstructured":"Garousi, V., Felderer, M., & M\u00e4ntyl\u00e4, M. V. (2016). The need for multivocal literature reviews in software engineering: complementing systematic literature reviews with grey literature. In Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (EASE \u201916) (p. 26). Association for Computing Machinery (ACM), Limerick, Ireland. https:\/\/doi.org\/10.1145\/2915970.2916008","DOI":"10.1145\/2915970.2916008"},{"key":"9754_CR17","unstructured":"Garousi, V., Joy, N., Jafarov, Z., Kele\u015f, A. B., De\u011firmenci, S., \u00d6zdemir, E., & Zarringhalami, R. (2025). AI-powered software testing tools: A systematic review and empirical assessment of their features and limitations. Preprint at https:\/\/arxiv.org\/abs\/2409.00411"},{"key":"9754_CR18","unstructured":"Google (2025). Google Programmable Search Engine. https:\/\/programmablesearchengine.google.com\/about\/"},{"key":"9754_CR19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jclinepi.2020.09.041","volume":"129","author":"C Hamel","year":"2021","unstructured":"Hamel, C., Michaud, A., Thuku, M., Skidmore, B., Stevens, A., Nussbaumer-Streit, B., & Garritty, C. (2021). Defining rapid reviews: a systematic scoping review and thematic analysis of definitions and defining characteristics of rapid reviews. Journal of Clinical Epidemiology, 129, 74\u201385. https:\/\/doi.org\/10.1016\/j.jclinepi.2020.09.041","journal-title":"Journal of Clinical Epidemiology"},{"key":"9754_CR20","doi-asserted-by":"publisher","unstructured":"Happe, A., & Cito, J. (2023). Getting pwn\u2019d by AI: Penetration testing with large language models. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE 2023) (pp. 2082\u20132086). Association for Computing Machinery (ACM), San Francisco, CA, USA. https:\/\/doi.org\/10.1145\/3611643.3613083","DOI":"10.1145\/3611643.3613083"},{"key":"9754_CR21","doi-asserted-by":"publisher","unstructured":"He, J., & Vechev, M. (2023) Large language models for code: Security hardening and adversarial testing. In ACM CCS. https:\/\/doi.org\/10.1145\/3576915.3623175","DOI":"10.1145\/3576915.3623175"},{"issue":"8","key":"9754_CR23","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1145\/3695988","volume":"33","author":"X Hou","year":"2024","unstructured":"Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024). Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 33(8), 220. https:\/\/doi.org\/10.1145\/3695988","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"9754_CR24","unstructured":"Index.dev (2024). How GenAI Transforms Software Development in 11 Ways. https:\/\/www.index.dev\/blog\/11-generative-ai-use-cases-software-development"},{"key":"9754_CR25","unstructured":"ISO\/IEC (2011). Systems and software engineering \u2013 systems and software quality requirements and evaluation (SQuaRE) \u2013 system and software quality models. Standard ISO\/IEC 25010:2011, International Organization for Standardization."},{"key":"9754_CR26","unstructured":"Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2024). SWE-bench: Can language models resolve real-world GitHub issues? In International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=VT9ovN-cfF"},{"key":"9754_CR27","doi-asserted-by":"publisher","unstructured":"Just, R., Jalali, D., & Ernst, M. D. (2014). Defects4j: A database of existing faults to enable controlled testing studies for java programs. In Proceedings of the 2014 International Symposium on Software Testing and Analysis (ISSTA \u201914) (pp. 437\u2013440). ACM. https:\/\/doi.org\/10.1145\/2610384.2628055","DOI":"10.1145\/2610384.2628055"},{"key":"9754_CR28","doi-asserted-by":"publisher","first-page":"112448","DOI":"10.1016\/j.jss.2025.112448","volume":"227","author":"I Kalouptsoglou","year":"2025","unstructured":"Kalouptsoglou, I., Siavvas, M., Ampatzoglou, A., Kehagias, D., & Chatzigeorgiou, A. (2025). Transfer learning for software vulnerability prediction using transformer models. Journal of Systems and Software, 227, 112448. https:\/\/doi.org\/10.1016\/j.jss.2025.112448","journal-title":"Journal of Systems and Software"},{"key":"9754_CR29","doi-asserted-by":"publisher","first-page":"107940","DOI":"10.1016\/j.infsof.2025.107940","volume":"189","author":"I Kalouptsoglou","year":"2026","unstructured":"Kalouptsoglou, I., Siavvas, M., Ampatzoglou, A., Kehagias, D., & Chatzigeorgiou, A. (2026). Locvul: Line-level vulnerability localization based on a sequence-to-sequence approach. Information and Software Technology, 189, 107940. https:\/\/doi.org\/10.1016\/j.infsof.2025.107940","journal-title":"Information and Software Technology"},{"key":"9754_CR30","doi-asserted-by":"publisher","unstructured":"Kang, S., Yoon, J., & Yoo, S. (2023). Large language models are few-shot testers: Exploring LLM-based general bug reproduction. In Proceedings of the 45th International Conference on Software Engineering (ICSE \u201923) (pp. 2312\u20132323). IEEE Press, Melbourne, Victoria, Australia. https:\/\/doi.org\/10.1109\/ICSE48619.2023.00194","DOI":"10.1109\/ICSE48619.2023.00194"},{"issue":"10","key":"9754_CR31","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1109\/TSE.2024.3450837","volume":"50","author":"S Kang","year":"2024","unstructured":"Kang, S., Yoon, J., Askarbekkyzy, N., & Yoo, S. (2024). Evaluating diverse large language models for automatic and general bug reproduction. IEEE Transactions on Software Engineering, 50(10), 2677\u20132694. https:\/\/doi.org\/10.1109\/TSE.2024.3450837","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9754_CR32","unstructured":"Kessel, M. (2025). Test-driven Software Experimentation with LASSO: an LLM Prompt Benchmarking Example. Preprint at https:\/\/arxiv.org\/abs\/2410.08911"},{"key":"9754_CR33","unstructured":"Kikuta, D., Ikeuchi, H., & Tajiri, K. (2025). ChaosEater: Fully Automating Chaos Engineering with Large Language Models. Preprint at arxiv:2501.11107"},{"key":"9754_CR34","unstructured":"Kitchenham, B. (2004). Procedures for performing systematic reviews. Technical Report TR\/SE-0401 and NICTA Technical Report 0400011T.1, Software Engineering Group, Department of Computer Science, Keele University and Empirical Software Engineering, National ICT Australia Ltd., Keele, UK and NSW, Australia. Joint Technical Report."},{"issue":"1","key":"9754_CR35","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159\u2013174. https:\/\/doi.org\/10.2307\/2529310","journal-title":"Biometrics"},{"key":"9754_CR36","doi-asserted-by":"publisher","unstructured":"Li, H., Wei, D., Moffitt, K .C., & Vasarhelyi, M. A. (2025). Addressing the last mile problem in open government data: Using AIS technologies to enhance governmental financial reporting. Journal of Emerging Technologies in Accounting. Forthcoming. https:\/\/doi.org\/10.2308\/JETA-2024-018","DOI":"10.2308\/JETA-2024-018"},{"key":"9754_CR37","doi-asserted-by":"publisher","unstructured":"Li, T.-O., Zong, W., Wang, Y., Tian, H., Wang, Y., Cheung, S.-C., & Kramer, J. (2023). Nuances are the key: Unlocking chatgpt to find failure-inducing tests with differential prompting. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE) (pp. 14\u201326). https:\/\/doi.org\/10.1109\/ASE56229.2023.00089","DOI":"10.1109\/ASE56229.2023.00089"},{"key":"9754_CR38","doi-asserted-by":"publisher","unstructured":"Lin, D., Koppel, J., Chen, A., & Solar-Lezama, A. (2017). Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge. In Proceedings of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH Companion \u201917) (pp. 55\u201356). ACM. https:\/\/doi.org\/10.1145\/3135932.3135941","DOI":"10.1145\/3135932.3135941"},{"key":"9754_CR39","doi-asserted-by":"publisher","unstructured":"Manganelli, A. (2025). Foundation models and generative ai applications: what competitive concerns? Available at SSRN 5242028. https:\/\/doi.org\/10.1080\/17441056.2026.2641378","DOI":"10.1080\/17441056.2026.2641378"},{"key":"9754_CR40","doi-asserted-by":"publisher","unstructured":"Mansur, E., Chen, J., Raza, M. A., & Wardat, M. (2024) RAGFix: Enhancing LLM code repair using RAG and Stack Overflow posts. In 2024 IEEE International Conference on Big Data (BigData) (pp. 7491\u20137496). IEEE, Washington, D.C., USA. https:\/\/doi.org\/10.1109\/BigData62323.2024.10825785","DOI":"10.1109\/BigData62323.2024.10825785"},{"key":"9754_CR41","doi-asserted-by":"publisher","unstructured":"Opara-Martins, J., Sahandi, R., & Tian, F. (2016). Critical analysis of vendor lock-in and its impact on cloud computing migration: a business perspective. Journal of Cloud Computing: Advances, Systems and Applications, 5(4). https:\/\/doi.org\/10.1186\/s13677-016-0054-z","DOI":"10.1186\/s13677-016-0054-z"},{"key":"9754_CR42","doi-asserted-by":"publisher","first-page":"100687","DOI":"10.1016\/j.simpa.2024.100687","volume":"21","author":"S Patel","year":"2024","unstructured":"Patel, S., Patil, K., & Chumchu, P. (2024). BHRAMARI: Bug driven highly reusable automated model for automated test bed generation and integration. Software Impacts, 21, 100687. https:\/\/doi.org\/10.1016\/j.simpa.2024.100687","journal-title":"Software Impacts"},{"key":"9754_CR43","doi-asserted-by":"publisher","unstructured":"Pena, C., Cartaxo, B., Steinmacher, I., Badampudi, D., Silva, D., Ferreira, W., Almeida, A., Kamei, F. K., & Soares, S. (2024). Comparing the efficacy of rapid review with a systematic review in the software engineering field. Journal of Software: Evolution and Process,37. https:\/\/doi.org\/10.1002\/smr.2748","DOI":"10.1002\/smr.2748"},{"key":"9754_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/9780470754887","volume-title":"Systematic Reviews in the Social Sciences: A Practical Guide","author":"M Petticrew","year":"2006","unstructured":"Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: A Practical Guide. Malden: Blackwell Publishing."},{"key":"9754_CR45","unstructured":"Phan, H. N., Nguyen, P. X., & Bui, N. D. (2024). Hyperagent: Generalist software engineering agents to solve coding tasks at scale. arXiv preprint arXiv:2406.11912"},{"key":"9754_CR46","doi-asserted-by":"publisher","unstructured":"Pizard, S., Lezama, J., Garc\u00eda, R., Vallespir, D., & Kitchenham, B. (2024). Using rapid reviews to support software engineering practice: a systematic review and a replication study. Empirical Software Engineering, 30(1). https:\/\/doi.org\/10.1007\/s10664-024-10545-6","DOI":"10.1007\/s10664-024-10545-6"},{"issue":"4","key":"9754_CR47","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TSE.2025.3543187","volume":"51","author":"Y Qin","year":"2025","unstructured":"Qin, Y., Wang, S., Lou, Y., Dong, J., Wang, K., & Li, X. (2025). Soapfl: A standard operating procedure for llm-based method-level fault localization. IEEE Transactions on Software Engineering, 51(4), 1173\u20131187. https:\/\/doi.org\/10.1109\/TSE.2025.3543187","journal-title":"IEEE Transactions on Software Engineering"},{"key":"9754_CR48","doi-asserted-by":"publisher","unstructured":"Rafi, S., Akbar, M. A., & Khan, A. A. (2025). Assessing software product quality in devops: An iso 25010:2023 perspective. In Proceedings of the 2025 Evaluation and Assessment in Software Engineering (EASE Companion \u201925). ACM, New York, NY, USA. https:\/\/doi.org\/10.1145\/3727967.3756847","DOI":"10.1145\/3727967.3756847"},{"key":"9754_CR49","doi-asserted-by":"publisher","unstructured":"Raghi, K. R., Sudha, K., M., S. A., Joshua,\u00a0S., & S. (2024). Software development automation using generative AI. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1\u20136. IEEE). https:\/\/doi.org\/10.1109\/ICERCS63125.2024.10894980","DOI":"10.1109\/ICERCS63125.2024.10894980"},{"key":"9754_CR50","doi-asserted-by":"publisher","unstructured":"Rocha, S., Feitosa, R., Galeno, L., & Travassos, G. H. (2025). A metaprotocol for a family of rapid multivocal reviews of generative ai in the software industry. In XXXIX Simp\u00f3sio Brasileiro de Engenharia de Software (pp. 804\u2013810). https:\/\/doi.org\/10.5753\/sbes.2025.11577","DOI":"10.5753\/sbes.2025.11577"},{"key":"9754_CR51","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1145\/3652154","volume":"33","author":"D Russo","year":"2024","unstructured":"Russo, D. (2024). Navigating the complexity of generative ai adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 33, 50. https:\/\/doi.org\/10.1145\/3652154","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"9754_CR52","unstructured":"Schmid, L., Hey, T., Armbruster, M., Corallo, S., Fuch\u00df, D., Keim, J., Liu, H., & Koziolek, A. (2025). Software Architecture Meets LLMs: A Systematic Literature Review. Preprint at arxiv:2505.16697"},{"key":"9754_CR53","unstructured":"SEDaily (2024). Weaving Generative AI into DevSecOps. https:\/\/softwareengineeringdaily.com\/2024\/04\/18\/gitlab-weaving-generative-ai-devsecops\/"},{"key":"9754_CR54","unstructured":"Shao, M., Xi, H., Rani, N., Udeshi, M., Putrevu, V. S. C., Milner, K., Dolan-Gavitt, B., Shukla, S. K., Krishnamurthy, P., Khorrami, F., Karri, R., & Shafique, M. (2025). CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution. Preprint at arxiv:2505.17107"},{"key":"9754_CR55","doi-asserted-by":"publisher","unstructured":"Sheng, Z., Wu, F., Zuo, X., Li, C., Qiao, Y., & Lei, H. (2024). Research on the LLM-driven vulnerability detection system using LProtector. In 2024 IEEE 4th International Conference on Data Science and Computer Application (ICDSCA) (pp. 192\u2013196). IEEE, Guilin, China. https:\/\/doi.org\/10.1109\/ICDSCA63855.2024.10859408","DOI":"10.1109\/ICDSCA63855.2024.10859408"},{"key":"9754_CR56","doi-asserted-by":"publisher","unstructured":"Shull, F., Singer, J., & Sj\u00f8berg, D. I. K. (Eds.) (2008). Guide to Advanced Empirical Software Engineering. Springer, London, UK. https:\/\/doi.org\/10.1007\/978-1-84800-044-5","DOI":"10.1007\/978-1-84800-044-5"},{"key":"9754_CR57","doi-asserted-by":"publisher","unstructured":"Siavvas, M., Kalouptsoglou, I., Gelenbe, E., Kehagias, D., & Tzovaras, D. (2024). Transforming the field of vulnerability prediction: Are large language models the key? In Proceedings of the 32nd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2024). https:\/\/doi.org\/10.1109\/MASCOTS64422.2024.10786575","DOI":"10.1109\/MASCOTS64422.2024.10786575"},{"key":"9754_CR58","unstructured":"Steenhoek, B., Rahman, M. M., Roy, M. K., Alam, M. S., Tong, H., Das, S., Barr, E. T., & Le, W. (2025). To Err is Machine: Vulnerability Detection Challenges LLM Reasoning. Preprint at https:\/\/arxiv.org\/abs\/2403.17218"},{"key":"9754_CR59","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1109\/TSE.2024.3382365","volume":"50","author":"Y Tang","year":"2024","unstructured":"Tang, Y., Liu, Z., Zhou, Z., & Luo, X. (2024). Chatgpt vs sbst: A comparative assessment of unit test suite generation. IEEE Transactions on Software Engineering, 50, 1340\u20131359. https:\/\/doi.org\/10.1109\/TSE.2024.3382365","journal-title":"IEEE Transactions on Software Engineering"},{"issue":"5","key":"9754_CR60","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1145\/3715003","volume":"34","author":"V Terragni","year":"2025","unstructured":"Terragni, V., Vella, A., Roop, P., & Blincoe, K. (2025). The future of AI-driven software engineering. ACM Transactions on Software Engineering and Methodology, 34(5), 120. https:\/\/doi.org\/10.1145\/3715003","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"9754_CR61","unstructured":"Travassos, G. H., Gurov, D., & Amaral, E. A. G. d. (2002). Introdu\u00e7\u00e3o \u00e0 engenharia de software experimental. Relat\u00f3rio T\u00e9cnico RT\u2013ES\u2013590\/02, Programa de Engenharia de Sistemas e Computa\u00e7\u00e3o, COPPE\/UFRJ, Rio de Janeiro, Brazil. https:\/\/pesc.coppe.ufrj.br\/index.php\/pt-BR\/publicacoes-pesquisa\/details\/15\/589"},{"key":"9754_CR62","doi-asserted-by":"publisher","unstructured":"Travassos, G. H., Rocha, S., Feitosa, R., Assis, F., Gon\u00e7alves, P., Gheventer, A., Galeno, L., Sasse, A., Guimar\u00e3es, J. C., Brito, C., & Wieland, J. P. (2025). Lessons learned from the use of generative ai in engineering and quality assurance of a web system for healthcare. In Proceedings of the XXIII Brazilian Symposium on Software Quality (SBQS \u201925). SBC, S\u00e3o Jos\u00e9 dos Campos, SP, Brazil. https:\/\/doi.org\/10.5753\/sbqs.2025.15015","DOI":"10.5753\/sbqs.2025.15015"},{"key":"9754_CR63","doi-asserted-by":"publisher","first-page":"107329","DOI":"10.1016\/j.infsof.2023.107329","volume":"164","author":"R Verdecchia","year":"2023","unstructured":"Verdecchia, R., Engstr\u00f6m, E., Lago, P., Runeson, P., & Song, Q. (2023). Threats to validity in software engineering research: A critical reflection. Information and Software Technology, 164, 107329. https:\/\/doi.org\/10.1016\/j.infsof.2023.107329","journal-title":"Information and Software Technology"},{"key":"9754_CR64","doi-asserted-by":"publisher","unstructured":"Wang, P., Loignon, A. C., Shrestha, S., Banks, G. C., & Oswald, F. L. (2025). Advancing organizational science through synthetic data: A path to enhanced data sharing and collaboration. Journal of Business and Psychology40, 771\u2013797. https:\/\/doi.org\/10.1007\/s10869-024-09997-w. Published online: 06 December 2024","DOI":"10.1007\/s10869-024-09997-w"},{"key":"9754_CR65","unstructured":"Washizaki, H. (Ed.) (2024). Guide to the Software Engineering Body of Knowledge (SWEBOK Guide), Version 4.0. IEEE Computer Society, Waseda University, Japan. https:\/\/www.swebok.org"},{"key":"9754_CR66","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-4625-2","volume-title":"Experimentation in Software Engineering: an Introduction","author":"C Wohlin","year":"2000","unstructured":"Wohlin, C., Runeson, P., H\u00f6st, M., Ohlsson, M. C., Regnell, B., & Wessl\u00e9n, A. (2000). Experimentation in Software Engineering: an Introduction. USA: Kluwer Academic Publishers."},{"issue":"FSE","key":"9754_CR67","doi-asserted-by":"publisher","first-page":"037","DOI":"10.1145\/3715754","volume":"2","author":"CS Xia","year":"2025","unstructured":"Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2025). Demystifying LLM-based software engineering agents. Proceedings of the ACM on Software Engineering, 2(FSE), 037. https:\/\/doi.org\/10.1145\/3715754","journal-title":"Proceedings of the ACM on Software Engineering"},{"key":"9754_CR68","doi-asserted-by":"publisher","unstructured":"Xia, C. S., Paltenghi, M., Le\u00a0Tian, J., Pradel, M., & Zhang, L. (2024). Fuzz4All: Universal fuzzing with large language models. In Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering (p. 126). Association for Computing Machinery (ACM), Lisbon, Portugal. https:\/\/doi.org\/10.1145\/3597503.3639121","DOI":"10.1145\/3597503.3639121"},{"key":"9754_CR69","doi-asserted-by":"publisher","first-page":"112731","DOI":"10.1016\/j.jss.2025.112731","volume":"234","author":"H Xu","year":"2026","unstructured":"Xu, H., Liu, H., Wu, Y., Kang, X., Chen, X., & Liu, Y. (2026). Exploring the potential and limitations of large language models for novice program fault localization. Journal of Systems and Software, 234, 112731. https:\/\/doi.org\/10.1016\/j.jss.2025.112731","journal-title":"Journal of Systems and Software"},{"key":"9754_CR70","doi-asserted-by":"publisher","unstructured":"Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K. R., & Press, O. (2024). SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https:\/\/doi.org\/10.52202\/079017-1601","DOI":"10.52202\/079017-1601"},{"key":"9754_CR71","unstructured":"Yang, J. (2025). Thematic Coding in Qualitative Research: A Practical Guide for Real Insights. https:\/\/www.usercall.co\/post\/thematic-coding-in-qualitative-research-a-practical-guide-for-real-insights"},{"key":"9754_CR72","unstructured":"Yu, Z., Zhang, H., Zhao, Y., Huang, H., Yao, M., Ding, K., & Zhao, J. (2025). OrcaLoca: An LLM Agent Framework for Software Issue Localization. Preprint at arxiv:2502.00350"},{"key":"9754_CR73","unstructured":"Zhang, Z., Chen, C., Liu, B., Liao, C., Gong, Z., Yu, H., Li, J., & Wang, R. (2024). Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code. Preprint at arxiv:2311.07989"},{"key":"9754_CR74","doi-asserted-by":"publisher","unstructured":"Zhou, X., Cao, S., Sun, X., & Lo, D. (2025). Large language model for vulnerability detection and repair: Literature review and the road ahead. ACM Transactions on Software Engineering and Methodology. https:\/\/doi.org\/10.1145\/3708522","DOI":"10.1145\/3708522"}],"container-title":["Software Quality Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-026-09754-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11219-026-09754-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11219-026-09754-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:54:41Z","timestamp":1783511681000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11219-026-09754-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":73,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["9754"],"URL":"https:\/\/doi.org\/10.1007\/s11219-026-09754-7","relation":{},"ISSN":["0963-9314","1573-1367"],"issn-type":[{"value":"0963-9314","type":"print"},{"value":"1573-1367","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"22 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All the participants were informed and freely signed a consent form to participate in the research.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"24"}}