Aug 2026· Acta Universitatis Sapientiae: Informatica· Vol 18· 0 citations· 33 references
TL;DR
It is suggested that process-oriented metrics, particularly those related to code testing and development history, capture defect patterns more effectively per feature than static code structure metrics, offering practical guidance for software quality assurance.
Abstract
Software defect prediction (SDP) aims to identify defect-prone code modules in order to optimize testing resources and improve software quality. While traditional approaches rely on software metrics derived from code structure, this paper proposes ET-SDP, an approach that enhances code embeddings using effort-related and test coverage metrics. We introduce three feature sets: the top-30 software metrics selected through clustering relevance ranking, 170 effort-related metrics capturing development process characteristics, and five test coverage metrics derived from unit tests. Our evaluation on 15 releases of Apache Calcite and 6 releases of Apache Ant-Ivy demonstrates that effort-related and test coverage metrics provide better defect prediction performance with far fewer features than traditional software metrics. In unsupervised clustering experiments, effort-based embeddings achieve better alignment with defect labels. In supervised classification, effort-related features achieve an AUC of 0.932 on Calcite and 0.883 on Ant-Ivy, outperforming the top-30 software metrics at comparable feature count (AUC of 0.632 and 0.587, respectively). Feature importance analysis reveals that test coverage metrics are the strongest predictors of defect proneness. These findings suggest that process-oriented metrics, particularly those related to code testing and development history, capture defect patterns more effectively per feature than static code structure metrics, offering practical guidance for software quality assurance.
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James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al.· 1 citation
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D. R. Patil, Varad Salgare, Devaj Arya et al.· International Journal for Re...· 0 citations
By streamlining workflows and fostering collaboration, this platform offers a scalable, cost- effective solution for SMEs and contributes to software engineering by demonstrating how integrated technologies can modernize development processes in resource limited contexts, with potential for broader adoption in Albania and beyond.
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Reza Noktesanj, Ali Nami, F. Amani et al.· journal of Health Research a...· 0 citations
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