arXiv subjects

Asif Mohammed Samir

Publications and source records attributed to Asif Mohammed Samir.

3 recordsLinked to original sources

arXiv2026

Improving IR-based Bug Localization with Semantics-Driven Query Reduction

Despite decades of research, software bug localization remains challenging due to heterogeneous content and inherent ambiguities in bug reports. Existing methods, such as Information Retrieval (IR)-based approaches, often attempt to match source documents to bug reports, overlooking the context and semantics of the source code. On the other hand, Large Language Models (LLMs) (e.g., Transformer models) show promising results in understanding both texts and code. However, they have not yet been adapted well to localize software bugs using bug reports. They could also be data or resource-intensive. To bridge this gap, we propose, IQLoc, a novel approach that capitalizes on the strengths of both IR and LLMs for bug localization. In particular, we leverage the transformer-based model's understanding of code semantics to reason about its suspiciousness and to reformulate search queries and thus enhance bug localization using Information Retrieval. To evaluate IQLoc, we refine the Bench4BL benchmark dataset and extend it by incorporating ~30% more recent bug reports, resulting in a benchmark containing ~7.5K bug reports. We evaluated IQLoc using three performance metrics and compare it against eight baseline techniques. Experimental results demonstrate its superiority, achieving up to 100.40% and 78.08% in MAP, 61.49% and 64.58% in MRR, and 76.98% and 100.90% in HIT@K for the test bug reports with random and time-wise splits, respectively. Moreover, IQLoc improves MAP by 118.70% for bug reports with stack traces, 111.87% for those that include code elements, and 127.45% for those containing only descriptions in natural language. By integrating program semantic understanding into Information Retrieval, IQLoc mitigates several longstanding challenges of traditional IR-based approaches in bug localization.

cs.SE↗

arXiv2026

Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition

Software bugs cost technology providers (e.g., AT&T) billions annually and cause developers to spend roughly 50% of their time on bug resolution. Traditional methods for bug localization often analyze the suspiciousness of code components (e.g., methods, documents) in isolation, overlooking their connections with other components in the codebase. Recent advances in Large Language Models (LLMs) and agentic AI techniques have shown strong potential for code understanding, but still lack causal reasoning during code exploration and struggle to manage growing context effectively, limiting their capability. In this paper, we present a novel agentic technique for bug localization -- CogniGent -- that overcomes the limitations above by leveraging multiple AI agents capable of causal reasoning, call-graph-based root cause analysis and context engineering. It emulates developers-inspired debugging practices (a.k.a., dynamic cognitive debugging) and conducts hypothesis testing to support bug localization. We evaluate CogniGent on a curated dataset of 591 bug reports using three widely adopted performance metrics and compare it against six established baselines from the literature. Experimental results show that our technique consistently outperformed existing traditional and LLM-based techniques, achieving MAP improvements of 23.33-38.57% at the document and method levels. Similar gains were observed in MRR, with increases of 25.14-53.74% at both granularity levels. Statistical significance tests also confirm the superiority of our technique. By addressing the reasoning, dependency, and context limitations, CogniGent advances the state of bug localization, bridging human-like cognition with agentic automation for improved performance.

cs.SE↗

arXiv2025

Improved IR-based Bug Localization with Intelligent Relevance Feedback

Software bugs pose a significant challenge during development and maintenance, and practitioners spend nearly 50% of their time dealing with bugs. Many existing techniques adopt Information Retrieval (IR) to localize a reported bug using textual and semantic relevance between bug reports and source code. However, they often struggle to bridge a critical gap between bug reports and code that requires in-depth contextual understanding, which goes beyond textual or semantic relevance. In this paper, we present a novel technique for bug localization - BRaIn - that addresses the contextual gaps by assessing the relevance between bug reports and code with Large Language Models (LLM). It then leverages the LLM's feedback (a.k.a., Intelligent Relevance Feedback) to reformulate queries and re-rank source documents, improving bug localization. We evaluate BRaIn using a benchmark dataset, Bench4BL, and three performance metrics and compare it against six baseline techniques from the literature. Our experimental results show that BRaIn outperforms baselines by 87.6%, 89.5%, and 48.8% margins in MAP, MRR, and HIT@K, respectively. Additionally, it can localize approximately 52% of bugs that cannot be localized by the baseline techniques due to the poor quality of corresponding bug reports. By addressing the contextual gaps and introducing Intelligent Relevance Feedback, BRaIn advances not only theory but also improves IR-based bug localization.

cs.SE↗