Nov 2026· Information and Software Technology· 0 citations· 70 references
TL;DR
Analysis of exception handling bugs in Python projects reveals systematic relationships between root causes and repair strategies, indicating that exception handling bugs often follow predictable patterns.
Abstract
Context: Exception handling mechanisms are designed to manage abnormal events that disrupt normal program execution, helping software recover from unexpected situations. However, defects in exception handling mechanisms can introduce errors, crashes, and unexpected behavior, reducing system reliability. Despite widespread adoption of Python, exception handling bugs in Python projects remain largely underexplored. Objectives: This study aims to investigate the characteristics of exception handling bugs in Python projects by analyzing their root causes, fix patterns, and their relationship with anti-patterns (exception handling anti-patterns). Methods: We conducted a large-scale empirical study on 550 open-source Python projects. Using our Exception Miner tool and manual validation, we analyzed 942 confirmed exception handling bugs identified from 1,649 bug candidates extracted from issue reports and associated commits. Results: Our analysis identified 12 root causes and 25 fix patterns. The most common root cause is Unhandled Exception , accounting for 50.64% of the analyzed cases. The most frequent repair strategies include adding exception handling blocks, changing exception types, and introducing appropriate raise conditions, which together account for approximately 60% of the fixes. A before-and-after analysis shows that many exception handling anti-patterns were preserved from the buggy version, while fixes still introduced an increase of 196 exception handling anti-patterns occurrences. Conclusion: The results reveal systematic relationships between root causes and repair strategies, indicating that exception handling bugs often follow predictable patterns. These findings provide insights to improve testing practices and design automated tools to detect and repair exception handling bugs in Python projects.
Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual information, an LLM may generate a correct patch for one bug but fail on another closely related bug. Why this happens remains poorly understood, and it is unclear how LLMs prioritize the diverse information in bug reports and whether model attention affects repair success. In this paper, we present the first empirical study of attention patterns in LLM-based program repair, providing interpretable insights into how models process bug reports and where their attention is concentrated during repair. We analyze 319 real-world Python and Java bugs from SWE-bench Verified and Multi-SWE-bench to study (RQ1) how model attention is distributed across bug report sections, (RQ2) how attention patterns within each section differ between successful and unsuccessful repairs, and (RQ3) how these patterns compare to information developers consider important for bug fixing. We find that successful repairs are characterized by diffused attention across multiple diagnostic components such as bug descriptions, stacktraces, and test cases, while failures often exhibit over-localized attention toward metadata such as version information. We further observe that stronger alignment between model attention and developer-identified key sections and phrases is associated with higher repair success. Our results provide the first empirical evidence that attention misallocation is a key factor in LLM-based APR failures, and offer actionable insights for designing more interpretable and reliable future APR systems.
Ramtin Ehsani, Irene Manotas, Saurabh Pujar et al.· 0 citations
Using data mining to extract frequent code patterns for bug detection has proven effective. However, prior studies have overlooked the prevalence of infrequent (rare) patterns, even though violations of such patterns can also lead to bugs. In this paper, we present LTMiner, which mines rare patterns from large-scale projects and detects potential bugs by checking for violations of these patterns. In practice, rare patterns far outnumber frequent ones and lack strong statistical support. Consequently, we face a pattern explosion, and many rare patterns and their violations are uninteresting. LTMiner addresses this by using instance-based ranking and filtering to prioritize violations of rare patterns. It further employs a large language model (LLM) as a domain expert to audit top-ranked violations; mined information supports in-context learning, and task decomposition and self-reflection mitigate possible hallucinations. This pipeline effectively curbs pattern explosion and false positives, uncovering previously unknown bugs in large-scale projects at an acceptable cost. Applied to Linux kernel 6.12.1, LTMiner identified 42 previously unknown bugs, 27 of which have been confirmed by developers. These results indicate that, although rare-pattern bugs are sparse, a considerable number remain and exhibit a non-negligible long tail. We believe that rare-pattern bugs constitute a promising blue ocean for bug detection.
Wentao Liang, Yanjun Wu, Xiang Ling et al.· Proceedings of the ACM on So...· 0 citations
Rust is a modern systems programming language that ensures memory safety through unique mechanisms, including ownership, borrowing, and lifetime annotations. These features prevent critical vulnerabilities but also impose strict constraints that many developers find difficult to understand. To mitigate this challenge, the Rust compiler, rustc, provides rich diagnostics and fix suggestions. However, recent studies reveal that diagnostic issues account for about 20% of all reported rustc bugs. Our analysis of rustc's suggestion bugs fixed over the past three years shows that most of them originated from errors in Rust-specific core modules, such as the type checker and borrow checker, rather than from simple mistakes in the general diagnostic logic, like suggesting an incorrect variable name or mismatched parentheses. The impact of diagnostic issues, especially bugs in rustc's fix suggestion, should not be underestimated, as they can mislead developers and reduce rustc's usability, and in severe cases may even lead to rustc crashes. Existing testing tools, however, provide little support for systematically evaluating the correctness and reliability of these suggestions. To address this gap, in this paper, we present SugBreaker, an automated testing framework specifically designed to validate rustc's suggestions. We propose a constraint-violation-guided mutation approach that injects type-related, borrow-related, and lifetime-related errors into valid Rust programs to trigger compiler diagnostics and iteratively verify the correctness of suggested fixes. SugBreaker has already detected 12 bugs, and 11 of them have been confirmed or fixed; all of them are triggered by different rustc error messages.Compared with a series of rustc testing baseline tools, SugBreaker achieves broader coverage of rustc's core checking modules and a higher suggestion trigger rate, which further confirms the effectiveness and efficiency of SugBreaker for testing rustc's fix suggestions.
Zixi Liu, Yang Feng, Jialiang Jiang et al.· Proceedings of the ACM on So...· 0 citations
Mobile applications frequently suffer from crash bugs that are triggered under specific GUI interaction sequences. Existing automated GUI testing techniques mainly emphasize increasing coverage through diverse exploration strategies, but they often fail to reach the precise interaction contexts that lead to crashes, resulting in low bug detection efficiency. This paper proposes TraceDroid, a novel automated GUI testing approach that leverages suspicious code-level signals to guide dynamic exploration. Instead of treating static analysis as an independent detection method, TraceDroid uses heuristic rules distilled from real crash reports to detect suspicious code segments, associate them with GUI widgets, and collect code-level interaction signals. It then constructs the Activity Transition Graph (ATG), performs rough path generation, and employs LLM-based executable path completion to produce a set of suspicious paths. Finally, TraceDroid executes these paths through global path planning, local path generation, and execution-aware monitoring to efficiently expose crashes. We evaluate TraceDroid on 70 real crash bugs across 42 open-source apps, comparing it with 15 state-of-the-art baselines. TraceDroid achieves the best performance, with a recall of 77%, exceeding the best baseline by 28%, while maintaining comparable or higher coverage. Furthermore, TraceDroid successfully detects 21 previously unknown crash bugs in 116 popular Google Play apps, of which 15 have been fixed and 6 confirmed by developers, demonstrating its effectiveness in real-world scenarios.
Mengzhuo Chen, Zhe Liu, Chunyang Chen et al.· Proceedings of the ACM on So...· 0 citations
Automated testing discovers vulnerabilities faster than developers can investigate and repair them, leaving an interval in which known memory corruptions remain exploitable. End- to-end LLM repair agents can shorten this interval, but they synthesize open-ended code changes and commonly validate them only by replaying a proof of concept (PoC). This weak oracle accepts patches that silence the observed crash by changing unrelated behavior, making unintended deployment risky. We present CodeMechanic, a bug-property-guided system for generating constrained mit- igations for spatial memory corruption. Instead of asking an LLM to generate a permanent repair, CodeMechanic reconstructs the violated memory-safety property from the crash, validates the dereferenced pointer and its buffer range, and inserts a local fail-stop guard before the dangerous access. The guard terminates execution when the boundary check fails. The resulting mitigation deliberately trades availability for security: it can convert potential remote code execution into controlled termination while developers investigate the root cause and prepare a permanent repair. CodeMechanic combines a two-dimensional static and dynamic context extractor with in-prompt debugging knowledge and stepwise val- idation to limit the effect of LLM errors. On 101 real-world ARVO bugs, the first attempt of CodeMechanic produces 47.6% more plausible patches (i.e., patches that pass PoC- replay validation) than the best baseline while using 91% fewer tokens. Manual audit further shows that CodeMechanic produces 3.4x - 4.3x more patches semantically equivalent to developer-written repairs.
Han Zheng, Rafaila Galanopoulou, Ilia Shumailov et al.· 0 citations
Repository-level documentation, such as READMEs, is often the first point of contact between users and a repository. When this documentation is incorrect, users may encounter runtime errors or waste their time debugging. We call such mistakes in repository-level documentation README bugs. Addressing README bugs is challenging because documentation mixes prose with code, its connection to the source of truth is loose, and finding a bug still leaves developers to craft a repair. This paper presents READU, an inconsistency-driven technique for just-in-time detection and repair of README bugs. The key insight behind READU is that README bugs often manifest as inconsistencies between documentation and another source of truth: either repository-internal facts, such as source code, or repository-external facts, such as external dependencies. READU applies a high-recall commit filter, runs internal and external consistency checkers in parallel, uses an alert judge to remove false positives, and automatically synthesizes documentation patches. On 6,000 recent commits from six popular repositories including Linux and Spring Boot, READU detects 244 true positives with 75% precision, while consuming less than $0.01 and less than one minute per commit, on average. Of these true positives, READU correctly repairs 217. We report 66 found README bugs, of which (so far) 44 are confirmed and 26 are fixed.
Doehyun Baek, Kilian Krampf, Michael Pradel· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
