Bug Localization from Bug Reports: A Multi-Objective Approach

Bug Localization from Bug Reports: A Multi-Objective Approach

Abstract

Figures & tables

Automated Software Engineering

From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning

Abstract

Automated Software Engineering

Explore similar work

From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning

BLAgent: Agentic RAG for File-Level Bug Localization

Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads

Waleed Ahmad; Mehtab Kiran Suddle; Maryam Bashir

cs.NEAug 27, 2026

Authors: Waleed Ahmad, Mehtab Kiran Suddle, Maryam Bashir

Organizations: FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan

Bug localization is a labor-intensive task, particularly in large software systems. When abnormal behavior occurs, developers must perform repetitive and time-consuming steps to identify faulty files. Previous studies have mainly focused on single-objective localization methods, many of which are limited to specific programming languages. In addition, relying solely on lexical similarity between source code and bug reports is often insufficient due to the natural language nature of bug descriptions. In this study, we propose a class-level automated multi-objective search-based system to identify and rank potentially buggy classes from bug reports. The main objective is to maximize similarity while minimizing the number of suggested faulty files. The evolutionary optimization algorithm SPEA-2 was applied to six open-source Java projects comprising more than 22,000 bug reports. The proposed approach was evaluated against two widely used algorithms, NSGA-II and MOEA/D. Results indicate that SPEA-2 achieved higher precision and recall than both multi-objective and single-objective baseline methods. The proposed recommender system successfully identified buggy classes or files for 88.5% of bug reports within the top 10 recommendations and 94% within the top 20. The effectiveness of the model was further validated on an industrial Android project written in Kotlin, demonstrating its adaptability across programming languages.

Figure 1: An Eclipse Bug Report Example (ID 378535)

Figure 2: A snippet extracted from the StackRenderer class

Author and Reference	Year	Algorithm	Technique	Dataset
Saha et al. [ 8 ]	2013	BLUiR (Bug Localization Using Information Retrieval )	Information Retrieval	Four Eclipse open source datasets [ 9 ]
Pandiyan et al. [ 10 ]	2015	Tabu Search Fault Localization with Path Branch and Bound (TSFL-PBB)	Search-based Software Engineering	2 UCI datasets
Rahman et al. [ 11 ]	2015	Improved Vector Space Model	Information Retrieval	ZXing, SWT, and Guava
Almhana et al. [ 1 ]	2016	NSGA-II	Search-based Software Engineering	Six open source datasets [ 7 ]
Lam et al. [ 42 ]	2017	DNNLOC (rVSM + DNN)	Combining IR with DNN	Six Benchmark datasets [ 7 ]
Xiao et al. [ 12 ]	2017	DeepLocator: Enchaned CNN using rTF-IDuF(Revised term frequency-user focused inverse document frequency)	Machine Learning	AspectJ, Eclipse, JDT, SWT and Tomcat projects

Table 1: Summary of Literature Review

Figure 4: An overview of the proposed multi-objective Search Recommendation System for bug reports

Figure 5: Flowchart of proposed SPEA-2 through opposition based learning

Figure 6: An example of vectors space model for text, each vocabulary word represents a dimension

Project Name	No. of Bug Reports	No. of files in project	No. of fixed classes per bug report	No. of API
SWT	4151	2056	3	161
AspectJ	593	4439	2	54
Tomcat	1056	1552	1	389
Birt	4178	6841	1	957
JDT	6274	8184	2	1329
Eclipse UI	6495	3464	2	1314

Table 2: Dataset Description

Figure 7: A code fragment written in XML programming language

Figure 8: A code fragment from class AdmissionActivity written in Kotlin programming language

Figure 9: Average Precision@10, Recall@10, and Accuracy@10 for NSGA-II, MOEA/D, and SPEA-2 on six open source java datasets listed in Table 2

Figure 10: Average Precision@10, Recall@10, and Accuracy@10 for NSGA-II and NSGA-II(Improved tokenization) on six open source java datasets listed in Table 2

Table 3: Precision@k

k	NSGA-II	NSGA-II using proposed tokenization	Lexical similarity	History-based Similarity	MOEA/D	SPEA-2
5	67	71	53	61	69.5	73
10	74	81	43	57	76	81
15	79.5	86	57	63	81.4	86.5
20	83	90	71	68	87	91.3

Table 4: Recall@k

k	NSGA-II	NSGA-II using proposed tokenization	Lexical similarity	History-based Similarity	MOEA/D	SPEA-2
5	61	65	49	32	62	68
10	74.4	85	72	63	83.5	88.5
15	82	92	81	70	90.8	91
20	87.3	95	86	69	92	94

Table 5: Accuracy@k

k	Precision			Recall			Accuracy
	NSGA-II	MOEA /D	SPEA-2	NSGA-II	MOEA /D	SPEA-2	NSGA-II	MOEA /D	SPEA-2
5	42.9	39	44	18.8	17	20.4	42	39	45
10	50	47	53.5	43.8	41.3	46.2	64	62.5	67
15	52.7	49.5	56	62.5	59	65	72	67	74
20	44.5	41	48	75	71.8	78.2	84	75.3	88.5

Table 6: Results on Industrial Android project

Figure 11: Precision@10, Recall@10, and Accuracy@10 for Lexical similarity, history-based similarity, MOEA/D, and SPEA-2 on six open source java datasets listed in Table 2

Figure 12: Precision@10, Recall@10, and Accuracy@10 for NSGA-II (Improved version), MOEA/D, and SPEA-2 on Kotlin-based Industrial Android project

NSGA-II (Improved version)	SPEA-2	MOEA/D	Lexical similarity	History-based Similarity
2874	3410	3123	5982	6863

Table 7: Efficiency of proposed algorithms (Running Time in milliseconds)

Automated Software Engineering uses AI, search, and verification methods to automate tasks across the software development lifecycle.

See all 82 papers

From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning
Sep 29, 2026Ankur Garg, Corey Yang-Smith, Rishav Rishav +2
University of Calgary, Canada
BLAgent: Agentic RAG for File-Level Bug Localization
May 18, 2026Md Afif Al Mamun, Gias UddinSoftware Engineering Agents LLM Reranking
University of Calgary, Canada · York University, Canada
Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads
Jun 8, 2026Nikolai RozanovMulti-Task Learning
Department of Computing, Imperial College London, London, UK

Sep 29, 2026J/K move · Enter open · S save

Ankur Garg, Corey Yang-Smith, Rishav Rishav, Ahmad Abdellatif+1

University of Calgary, Canada

Open paper AskPDF arXiv

We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across multiple levels of granularity: files, functions, and lines of code. To mirror developer's natural top-down debugging workflows, C2C integrates semantic retrieval and Hierarchical Reinforcement Learning (HRL) in a two-stage process. First, it performs recall-oriented retrieval of buggy candidates via semantic vector similarity search using bug-report text, including available stack-trace information, against a database of embeddings, where the embeddings are fine-tuned via contrastive learning with CodeBERT. Building on this reduced search space, the HRL framework incrementally localizes bugs, reasoning from files to functions and ultimately to individual lines of code. Unlike prior approaches which operate at a single granularity, C2C enables multi-resolution localization while maintaining contextual consistency across decisions. Experiments on real-world Java and Python datasets demonstrate that C2C improves retrieval precision and localization accuracy. Ablation studies further highlight the contributions of hierarchical decomposition, structured learning signals, and reward shaping in advancing multi-level bug localization.

NSGA-II using proposed tokenization

Lexical similarity

History-based Similarity