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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Xiaoyu Wu, Jingzheng Ling, Xiang Luo, Tianyue Wu, Yanjun Institute of Software Chinese Academy of Sciences Intelligent Software Research Center China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350330717

In recent years, deep learning has been widely applied in the research of automatic program repair, namely, learning-based program repair, which treats the program repair task as either a neural machine translation problem or a transformation toward the abstract syntax tree of the program. However, all existing learning-based program repair solutions have not adequately modeled the deep semantic information of the programs. To be specific, those solutions based on neural machine translation do not involve any semantic information of the programs at all, while other solutions based on abstract syntax tree transformations also do not involve information such as control flow and data flow. Additionally, the neural networks they use are also unable to sufficiently learn the semantic information. As a result, they cannot fix some bugs that involve semantic *** this paper, we propose a new learning-based program repair approach called SGEPR. SGEPR uses a novel intermediate representation named sequence code property graph (SCPG) to model program semantic information, which can cover multiple types of semantic information in the program. SGEPR then utilizes a graph neural network based on the attention mechanism to learn the semantic information within SCPG. Finally, a well-designed prediction network is employed to generate the repair patches for the buggy program. To evaluate the performance of SGEPR, we conducted comparative experiments on a real-world dataset and the results show that SGEPR can achieve a top-3 accuracy of 56.85%, which is 15.69% higher than the state-of-the-art learning-based program repair method. © 2023 IEEE.

关键词： automatic program repair graph neural network program representation

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Let's Talk With Developers, Not About Developers: A Review of automatic program repair Research

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第1期49卷 419-436页

作者： Winter, Emily Nowack, Vesna Bowes, David Counsell, Steve Hall, Tracy Haraldsson, Saemundur Woodward, John Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England Brunel Univ London Dept Comp Sci London UB8 3PH England Univ Stirling Dept Comp Sci & Math Stirling FK9 4LA England Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

automatic program repair (APR) offers significant potential for automating some coding tasks. Using APR could reduce the high costs historically associated with fixing code faults and deliver significant benefits to software engineering. Adopting APR could also have profound implications for software developers ' daily activities, transforming their work practices. To realise the benefits of APR it is vital that we consider how developers feel about APR and the impact APR may have on developers ' work. Developing APR tools without consideration of the developer is likely to undermine the success of APR deployment. In this paper, we critically review how developers are considered in APR research by analysing how human factors are treated in 260 studies from Monperrus 's Living Review of APR. Over half of the 260 studies in our review were motivated by a problem faced by developers (e.g., the difficulty associated with fixing faults). Despite these human-oriented motivations, fewer than 7% of the 260 studies included a human study. We looked in detail at these human studies and found their quality mixed (for example, one human study was based on input from only one developer). Our results suggest that software developers are often talked about in APR studies, but are rarely talked with. A more comprehensive and reliable understanding of developer human factors in relation to APR is needed. Without this understanding, it will be difficult to develop APR tools and techniques which integrate effectively into developers ' workflows. We recommend a future research agenda to advance the study of human factors in APR.

关键词： Human factors software development automatic program repair

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BUGFIX: towards a common language and framework for the automatic program repair community 5

BUGFIX: towards a common language and framework for the Auto...

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5th ACM/IEEE International Workshop on Automated program repair (APR)

作者： Meyer, Bertrand Kananchuk, Viktoryia Huang, Li Constructor Inst Schaffhausen Switzerland

ISBN: (纸本)9798350353020;9798400705779

Techniques of automatic program repair (APR) have the potential of thoroughly facilitating the task of producing quality software. After a promising start, however, progress in making APR practical has been hindered by the lack of a common framework to support the multiplicity of APR ideas and tools, and of target programming languages and environments. In this position paper we outline a general framework to enable the APR community to benefit from each other's advances, in particular through a standard language for describing bugs and their fixes. Such a common framework - which is also applicable to work on fault seeding - could be a tremendous benefit to researchers and developers of Interactive Development Environments (IDEs) who are working to make APR an effective part of the software developer's practical experience.

关键词： automatic program repair Debugging Integrated Development Environments Software tools program transformation Bug seeding Software quality

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An Empirical Study of automatic program repair Techniques for Injection Vulnerabilities 40

An Empirical Study of Automatic Program Repair Techniques fo...

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40th International Conference on Software Maintenance and Evolution

作者： Zhu, Tingwei Xu, Tongtong Liu, Kui Zhou, Jiayuan Hu, Xing Xia, Xin Zhang, Tian Lo, David Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Huawei Technol Co Ltd Markham ON Canada Zhejiang Univ Hangzhou Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798350395693;9798350395686

Injection vulnerabilities are among the most serious and dangerous security defects, as they can be exploited by attackers to inject malicious inputs and carry out cybercrimes. Timely fixing of injection vulnerabilities is crucial. However, manual repairs of injection vulnerabilities often require specialized knowledge and are prone to errors, posing a challenge and a heavy burden on developers. In recent years, Automated program repair (APR) techniques have shown promising momentum in automatically fixing general defects. Yet, there has been no research on how APR techniques perform in repairing injection vulnerabilities. Therefore, in this paper, we conduct an empirical study. We first construct a benchmark for injection vulnerability repair and evaluate several representative state-of-the-art APR approaches on this benchmark. The results show that existing APR tools do not adequately support the repair of injection vulnerabilities. To investigate the underlying reasons, we compare the characteristics of patches for injection vulnerabilities and general defects, and explore whether the plastic surgery hypothesis widely used in APR still holds for injection vulnerabilities. The results reveal that fixing injection vulnerabilities is more complex than fixing general defects due to significant differences in the characteristics of their patches. Additionally, the support for the plastic surgery hypothesis is much lower in the context of injection vulnerability repair. We also analyzed developers' intentions when fixing injection vulnerabilities. Finally, we summarize the implications and point out potential research directions for injection vulnerability repair.

关键词： Injection vulnerability automatic program repair empirical study

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Improving Search-Based automatic program repair With Neural Machine Translation

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IEEE ACCESS 2022年 10卷 51167-51175页

作者： Li, Dongcheng Wong, W. Eric Jian, Mingyong Geng, Yi Chau, Matthew Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

The challenge of automatically repairing bugs in programs to reduce debugging expenses and increase program quality is known as automated program repair. To overcome this issue, test-suite-based repair techniques use a specified test suite as an oracle and alter the input faulty program to pass the full test suite. GenProg is a well-known example of this kind of repair, in which genetic programming is used to reorder the statements already present in the faulty program. However, recent practical experiments suggest that GenProg's performance, notably for Java, is not sufficient. Improved program dependability necessitates the use of automatic program repair techniques. Template-based program repair techniques have recently been combined with search-based techniques to solve program issues automatically. Although intriguing, it has two fundamental drawbacks: Its search space often lacks the correct solution, and the technique disregards program expertise, such as precise code language. Compared with the template-based program repair approach, existing neural-machine-translation-based approaches are not limited by these constraints due to their ability to learn and generate new solutions. We propose an approach that combines a search-based automatic program repair technique with a neural-machine-translation-based approach. More specifically, we use both redundancy assumption and sequence-to-sequence learning of correct patches as the source for potential fix statements that feed into a multiobjective evolutionary search algorithm to find test-suite-adequate patches. In this work, a novel framework called ARJANMT is introduced for automatically repairing Java programs. Two sets of controlled experiments are conducted on 410 bugs from two benchmarks to investigate the repairability and correctness of our proposed framework. A comparison between state-of-the-art automatic program repair frameworks is made. The experimental results indicate that combining those two types

关键词： Maintenance engineering Codes Computer bugs Java Genetic programming Termination of employment Task analysis Search-based software engineering automatic program repair multi-objective optimization genetic programming neural machine translation

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Mining Fix Patterns with Context Information for automatic program repair 3

Mining Fix Patterns with Context Information for Automatic P...

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3rd IEEE/ACM International Workshop on Automated program repair (APR)

作者： Huyen, Phan Thi Thanh Yasuda, Kazuya Itoh, Shinji Hitachi Ltd Yokohama Kanagawa Japan

ISBN: (纸本)9798350302141

Template-based automatic program repair (APR) using pre-defined fix patterns for generating patches is common in APR literature and is implemented in many APR tools. As the existing APR tools can fix only a limited number of bugs, automatic mining fix patterns from human-written fixes is an appropriate approach to expand the fix pattern set at low cost. On the other hand, a large number of fix patterns do not ensure that a template-based APR tool will become more effective if the buggy context describing the condition for choosing a fix pattern to generate fixes for a buggy code is too generic. Because the state-of-the-art fix patterns just provide a generic code context for matching a fix pattern, the number of matched patterns and therefore the number of generated patches is exceptionally large. Therefore, we mine human-written fixes automatically to not only extract new fix patterns but also clarify the buggy contexts of the bug fix instances forming each fix pattern. Different from the previous works, we propose to use test execution information, such as exception types and execution paths, in addition to code contexts, as the buggy contexts of the fix patterns. For mining strategy, we choose an agglomerative hierarchical clustering algorithm with our custom distance metric to group the bug fix instances that make the same or similar changes into the same cluster. The evaluation result on the bug set of two APR benchmarks Defects4J and Bears reveals that the fix patterns extracted from the clusters containing at least two similar bug fix instances are matched with most of the state-ofthe-art fix patterns. Moreover, 11 new fix patterns are found. The buggy contexts, including the code context and the test execution information, of all bug fix instances forming each fix pattern are also clarified. Our fix patterns with the new buggy context information are expected to help the template-based APR tools generate and verify patches more effectively.

关键词： automatic program repair fix pattern edit script AST buggy context code context test execution information hierarchical clustering custom distance metric

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Better automatic program repair by Using Bug Reports and Tests Together 23

Better Automatic Program Repair by Using Bug Reports and Tes...

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45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Motwani, Manish Brun, Yuriy Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781665457019

Automated program repair is already deployed in industry, but concerns remain about repair quality. Recent research has shown that one of the main reasons repair tools produce incorrect (but seemingly correct) patches is imperfect fault localization (FL). This paper demonstrates that combining information from natural-language bug reports and test executions when localizing faults can have a significant positive impact on repair quality. For example, existing repair tools with such FL are able to correctly repair 7 defects in the Defects4J benchmark that no prior tools have repaired correctly. We develop, Blues, the first information-retrieval-based, statement-level FL technique that requires no training data. We further develop RAFL, the first unsupervised method for combining multiple FL techniques, which outperforms a supervised method. Using RAFL, we create SBIR by combining Blues with a spectrum-based (SBFL) technique. Evaluated on 815 real-world defects, SBIR consistently ranks buggy statements higher than its underlying techniques. We then modify three state-of-the-art repair tools, Arja, SequenceR, and SimFix, to use SBIR, SBFL, and Blues as their internal FL. We evaluate the quality of the produced patches on 689 real-world defects. Arja and SequenceR significantly benefit from SBIR: Arja using SBIR correctly repairs 28 defects, but only 21 using SBFL, and only 15 using Blues;SequenceR using SBIR correctly repairs 12 defects, but only 10 using SBFL, and only 4 using Blues. SimFix, (which has internal mechanisms to overcome poor FL), correctly repairs 30 defects using SBIR and SBFL, but only 13 using Blues. Our work is the first investigation of simultaneously using multiple software artifacts for automated program repair, and our promising findings suggest future research in this directions is likely to be fruitful.

关键词： automatic program repair Debugging fault localization Information retrieval based fault localization

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automatic program repair with Improved Transformer 23

Automatic Program Repair with Improved Transformer

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Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning

作者： Zeyu Sun Huaibo Sun Anhui University of Technology China School of Mathematics and Statistics Fuyang Normal University China

ISBN: (纸本)9798400707544

The rise of Deep Learning (DL) has led researchers to explore techniques for automatic program repair (APR) using DL. However, existing methods for APR suffer from issues such as small datasets, low accuracy, and long execution times. In this paper, we propose a method based on transformer for APR called TLinearFFN, and we augment the dataset. The results show that on the original dataset, our model achieved a repair accuracy improvement of approximately 13.81% compared to previous studies, and approximately 2.78% compared to the original transformer model. Unfortunately, our augmented dataset did not have a positive impact on accuracy. To summarize, our results demonstrate that the TLinearFFN method has excellent code repair performance, provides a more efficient and accurate solution for code repair.

关键词： automatic program repair Deep learning Transformer

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Applying Token Tagging to Augment Dataset for automatic program repair

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第5期18卷 628-636页

作者： Hu, Huimin Lee, Byungjeong Univ Seoul Dept Comp Sci Seoul South Korea

automatic program repair (APR) techniques focus on automatically repairing bugs in programs and providing correct patches for developers, which have been investigated for decades. However, most studies have limitations in repairing complex bugs. To overcome these limitations, we developed an approach that augments datasets by utilizing token tagging and applying machine learning techniques for APR. First, to alleviate the data insufficiency problem, we augmented datasets by extracting all the methods (buggy and non-buggy methods) in the program source code and conducting token tagging on non-buggy methods. Second, we fed the preprocessed code into the model as an input for training. Finally, we evaluated the performance of the proposed approach by comparing it with the baselines. The results show that the proposed approach is efficient for augmenting datasets using token tagging and is promising for APR.

关键词： Augment Dataset automatic program repair Machine Learning Token Tagging

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DALO-APR: LLM-based automatic program repair with data augmentation and loss function optimization

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷

作者： Wang, Shaosheng Lu, Lu Qiu, Shaojian Tian, Qingyan Lin, Haishan South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Artificial Intelligence & Digital Econ L Guangzhou 510320 Guangdong Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Guangdong Prov Key Lab Tunnel Engn Safety & Emerge Guangzhou 510440 Guangdong Peoples R China

automatic program repair (APR) has made significant strides with the advent of large language models (LLMs) such as T5 and CodeT5. However, LLM-based APR models may rely on repetitive repair patterns due to limited training data diversity, resulting in suboptimal performance. Additionally, common loss functions, such as cross-entropy, may not fully prioritize repair locations or optimize the model's output probability distribution to favor more accurate repair candidates. To address these challenges, this paper proposes a method for LLM-Based APR with Data Augmentation and Loss Function Optimization (DALO-APR). The data augmentation strategy expands the variety of repair patterns by randomly deleting, inserting, swapping tokens, and injecting errors. The optimized loss function helps the model rank more accurate repair candidates higher. Experimental results on Java, JavaScript, Python, and C datasets demonstrate that DALO-APR improves both error localization and bug fixing. Compared to baseline models, DALO-APR shows improvements across multiple metrics, especially with a 105.65% increase in 100% accuracy.

关键词： automatic program repair Large language models Data augmentation Loss function

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