An empirical study of deep transfer learning-based program repair for Kotlin projects

Kim, Misoo; Kim, Young-Kyoung; Jeong, Hohyeon; Heo, Jin-Seok; Kim, Sungoh; Chung, HyunHee; Lee, Eunseok

doi:10.1145/3540250.3558967

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…The effectiveness of the transformer-based program repair model has been experimentally demonstrated in both encoder-decoder families 2 (Li et al, 2022;Kim et al, 2022b;Wang et al, 2021;Berabi et al, 2021) and decoder-only families (Jesse et al, 2023;Joshi et al, 2022;Prenner and Robbes, 2021), with their correct patch generation accuracy. The program repair model is trained to transform the input buggy code into a fixed code (that is, a patch).…”

Section: Transformer For Program Repairmentioning

confidence: 99%

Improving Transformer-based Program Repair Model through False Behavior Diagnosis

Kim,

Lee

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Research on automated program repairs using transformer-based models has recently gained considerable attention. The comprehension of the erroneous behavior of a model enables the identification of its inherent capacity and provides insights for improvement. However, the current landscape of research on program repair models lacks an investigation of their false behavior. Thus, we propose a methodology for diagnosing and treating the false behaviors of transformer-based program repair models. Specifically, we propose 1) a behavior vector that quantifies the behavior of the model when it generates an output, 2) a behavior discriminator (BeDisc) that identifies false behaviors, and 3) two methods for false behavior treatment. Through a large-scale experiment on 55,562 instances employing four datasets and three models, the BeDisc exhibited a balanced accuracy of 86.6% for false behavior classification. The first treatment, namely, early abortion, successfully eliminated 60.4% of false behavior while preserving 97.4% repair accuracy. Furthermore, the second treatment, namely, masked bypassing, resulted in an average improvement of 40.5% in the top-1 repair accuracy. These experimental results demonstrated the importance of investigating false behaviors in program repair models.

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Section: Transformer For Program Repairmentioning

confidence: 99%

Improving Transformer-based Program Repair Model through False Behavior Diagnosis

Kim,

Lee

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…In addition to the above technical papers, Kim et al [73] empirically investigate the performance of TFix in fixing errors from industrial Samsung Kotlin projects detected by a static analysis tool SonarQube. Mohajer et al [105] conduct a more comprehensive study of LLMs in the static code analysis domain, and propose SkipAnalyzer, an LLM-based powered tool to perform three related tasks: detecting bugs, filtering false positive warnings, and patching the detected bugs.…”

Section: Static Warningsmentioning

confidence: 99%

“…Similar to most traditional learning-based APR, this type of input regards APR as an NMT task, which translates a sentence from one source language (i.e., buggy code) to another target language (i.e., fixed code). Such representation directly feeds LLMs with the buggy code snippet and has been typically employed to train LLMs with supervised learning in semantic bugs [28,101,206] security veulnerabilities [39,188], and static warnings [73]. For example, Zhang et al [188] investigate the performance of three bug-fixing representations (i.e., context, abstraction, and tokenization) to fine-tune five LLMs for vulnerability repair.…”

Section: What Input Forms Are Software Bugs Transformed Into When Uti...mentioning

confidence: 99%

“…Particularly, they utilize "Buggy line:" and "Context:" to denote the buggy and contextual code, and they utilize "The fixed code is:" to query a T5-based model to generate candidate patches according to the previous input. Besides, TFix [7], Zirak et al [206] and Kim et al [73] represent all valuable information about the bug as a single piece of text, including bug type, bug message, bug line, and bug context. Furthermore, InferFix [69] and RAP-Gen [153] construct prompts by retrieving relevant repair examples from an external codebase.…”

Section: What Input Forms Are Software Bugs Transformed Into When Uti...mentioning

confidence: 99%

“…Recently, inspired by the advances of Deep Learning (DL), an increasing number of learning-based APR techniques have been proposed that utilize neural network models to automatically learn bug-fixing patterns [18, 66, 84, 85, 96, 144, 176-178, 203, 204]. Thanks to the powerful ability of DL models to learn hidden repair patterns from massive code corpora, learning-based APR has achieved remarkable performance in the last couple of years [185], attracting considerable attention from both academia and industry [69,70,73].…”

Section: Introductionmentioning

confidence: 99%

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Introduction to the Department of Cardiology in Nanjing First Hospital of Nanjing Medical University, China

Zhang

Chen

2020

European Heart Journal

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Reclosure of ruptured incision after peroral endoscopic myotomy using endoloops and metallic clipsSince Inoue et al. introduced peroral endoscopic myotomy (POEM) into a clinic to treat esophageal achalasia in 2010, the procedure has been carried out in many countries around the world. 1 As more and more POEM is being done, associated technical difficulties and complications may occur. 2 To our knowledge, the present study is the first to report incision rupture after POEM.A 37-year-old man presented to our academic center after experiencing 25 years of dysphagia and 2 months of exacerbation. Barium swallow examination and esophageal manometry diagnosed the patient with type I esophageal achalasia and he agreed to receive POEM.POEM was carried out using the standard technique. After the operation, the patient received routine postoperative care. On the third day after the procedure, the patient had a fever (38.9°C, white blood cell count 18.24 × 10 9 /L, % neutrophils 95.3%) and X-ray showed the absence of several metal clips at the proximal end of the longitudinal incision which revealed the incision rupture.Gastroesophageal endoscopy showed that the middle and proximal parts of the incision was ruptured. It was not possible to reclose the incision with routine clips because of the swollen mucosa around the defect. On endoscopy, an endoloop was inserted and snared the remaining clips in the distal part. In the middle, four clips were anchored onto the defect margins at full thickness and another endoloop was inserted to snare the clips tightly. The same procedure was done in the proximal part (Figs 1,2). After monitoring the patient's condition for several days, he was discharged without any complaints or complications.In the present study, we propose reclosure of a mucosal incision after POEM using conventional endoloops and hemostatic clips. It could reclose the incision regardless of the swollen tissue or the size of the longitudinal incision.

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REFERENT: Transformer-based Feedback Generation using Assignment Information for Programming Course

Heo

Jeong

Choi

et al. 2023

2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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An empirical study of deep transfer learning-based program repair for Kotlin projects

Cited by 8 publications

References 38 publications

Improving Transformer-based Program Repair Model through False Behavior Diagnosis

Improving Transformer-based Program Repair Model through False Behavior Diagnosis

Introduction to the Department of Cardiology in Nanjing First Hospital of Nanjing Medical University, China

REFERENT: Transformer-based Feedback Generation using Assignment Information for Programming Course

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