Aug 2026· Revista Eletrônica de Iniciação Científica em Computação· Vol 24, pp. 574-581· 0 citations· 13 references
TL;DR
An empirical study using real-world data investigating the feasibility of Retrieval-Augmented Generation (RAG) to support interactive exploration of software defect repositories to improve decision-making in quality assurance and software development by enabling scalable and intelligent exploration of software failures.
Abstract
A defect database is a structured collection of information about software bugs, errors, and their resolutions throughout the development lifecycle. These databases have valuable information for improving software quality, but their growing volume and complexity make manual analysis inefficient and error-prone. This paper presents an empirical study using real-world data investigating the feasibility of Retrieval-Augmented Generation (RAG) to support interactive exploration of software defect repositories. The study is structured with reference to selected phases of the CRISP-DM framework, which was used as an organizational guideline. Using a real-world software defect database as the empirical basis, the approach automates insight extraction from defect repositories, aiming to improve decision-making in quality assurance and software development by enabling scalable and intelligent exploration of software failures.
Current research is summarized to identify key gaps and future directions to optimize LLM based APR are proposed, to assure its reliability and scalability in real world software development.
Fatmaelzahra Hamdi, Ramadam Moawad, A. Mohsen· Journal of universal compute...· 0 citations
Algorithm-Driven Development is introduced, a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior that provides systematic coverage of functional scenarios from the outset of development.
Philippe Jawish, Pierre Evrard, Alexandre Lemerle et al.· Journal of Systems and Softw...· 0 citations
A Systematic Mapping Study on the quality of AI-based software identifies six recurring challenge categories, with the most prominent being limitations in existing quality assessment models followed by issues in non-functional requirement management, quality-aware development, and quality assurance.
Maryum Hamdani, Mateen Ahmed Abbasi, Marko Jäntti et al.· 0 citations
This Systematic Literature Review examines prompt engineering in automatic code generation using large language models (LLMs) and shows that prompt engineering has been established as a key discipline for optimizing interaction with LLMs and improve the accuracy, robustness, and applicability of the generated code.
E. Camacho, Y. Gutierrez, César Pardo· 0 citations
Aim.
Developing a reproducible methodology for identifying source code «hotspots» based on Git data to better understand development history, identifying flawed areas and architectural deficiencies.
Methods.
The study employs relational analysis to enable end-to-end analytics of development history by linking the entities File → Commit → Pull Request → Issue. Three key criteria are used: file change frequency, number of contributors, and the number of bugfix changes.
Results.
The methodology was tested on real API service data from two intelligent transportation systems. Ranked lists for each criterion were obtained along with their association with the system’s functional components.
Conclusion.
An analysis of a relational repository provides valuable insights into development history, helps identify code areas requiring greater attention, and thereby improves software quality.
Unknown authors· Dependability· 0 citations
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