This paper outlines techniques and research directions for scaling genetic improvement and automated program repair, highlighting possible directions for future work and open challenges.
本文概述了基因改良和自动程序修复的技术和研究方向，强调了未来工作的可能方向和开放的挑战。
{"title":"Scaling Genetic Improvement and Automated Program Repair","authors":"M. Harman","doi":"10.1145/3524459.3527353","DOIUrl":"https://doi.org/10.1145/3524459.3527353","url":null,"abstract":"This paper outlines techniques and research directions for scaling genetic improvement and automated program repair, highlighting possible directions for future work and open challenges.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many techniques have contributed to the advancement of auto-mated program repair, such as: generate and validate approaches, constraint-based solvers and even neural machine translation. Si-multaneously, artificial intelligence has allowed the creation of general-purpose pre-trained models that support several down-stream tasks. In this paper, we describe a technique that takes advantage of a generative model - CodeGPT - to automatically repair buggy programs by making use of its code completion capa-bilities. We also elaborate on where to perform code completion in a buggy line and how we circumvent the open-ended nature of code generation to appropriately fit the new code in the original pro-gram. Furthermore, we validate our approach on the ManySStuBs4J dataset containing real-world open-source projects and show that our tool is able to fix 1739 programs out of 6415 - a 27% repair rate. The repaired programs range from single-line changes to multiple line modifications. In fact, our technique is able to fix programs which were missing relatively complex expressions prior to being analyzed. In the end, we present case studies that showcase different scenarios our technique was able to handle.
{"title":"Framing Program Repair as Code Completion","authors":"Francisco Ribeiro, Rui Abreu, João Saraiva","doi":"10.1145/3524459.3527347","DOIUrl":"https://doi.org/10.1145/3524459.3527347","url":null,"abstract":"Many techniques have contributed to the advancement of auto-mated program repair, such as: generate and validate approaches, constraint-based solvers and even neural machine translation. Si-multaneously, artificial intelligence has allowed the creation of general-purpose pre-trained models that support several down-stream tasks. In this paper, we describe a technique that takes advantage of a generative model - CodeGPT - to automatically repair buggy programs by making use of its code completion capa-bilities. We also elaborate on where to perform code completion in a buggy line and how we circumvent the open-ended nature of code generation to appropriately fit the new code in the original pro-gram. Furthermore, we validate our approach on the ManySStuBs4J dataset containing real-world open-source projects and show that our tool is able to fix 1739 programs out of 6415 - a 27% repair rate. The repaired programs range from single-line changes to multiple line modifications. In fact, our technique is able to fix programs which were missing relatively complex expressions prior to being analyzed. In the end, we present case studies that showcase different scenarios our technique was able to handle.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131385736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The field of Automated Program Repair (APR) has seen significant growth in the past decade. As the field progressed, the number of templates used by APR tools has grown substantially to increase the number of patches included within the domain each tool finds fixable, thus increasing their fixing capability. However, this height-ened potential was not free: new techniques paid by using greater computational resources and time to look over an enlarged repair space. In this paper, we look to curtail this trend by using language models (LMs) to provide guidance about whether a generated patch is natural. By prioritizing patches that generate natural code, which has been demonstrated in prior work to be related to correctness, we can reduce the number of patches that must be inspected to find the first correct patch. We evaluate this prioritization scheme over five APR tools, and find that we can reduce the number of patches that must be inspected in up to 70% of bugs and reduce the total number of patches inspected by up to two-thirds, paving the way for lower-cost program repair.
{"title":"Language Models Can Prioritize Patches for Practical Program Patching","authors":"Sungmin Kang, S. Yoo","doi":"10.1145/3524459.3527343","DOIUrl":"https://doi.org/10.1145/3524459.3527343","url":null,"abstract":"The field of Automated Program Repair (APR) has seen significant growth in the past decade. As the field progressed, the number of templates used by APR tools has grown substantially to increase the number of patches included within the domain each tool finds fixable, thus increasing their fixing capability. However, this height-ened potential was not free: new techniques paid by using greater computational resources and time to look over an enlarged repair space. In this paper, we look to curtail this trend by using language models (LMs) to provide guidance about whether a generated patch is natural. By prioritizing patches that generate natural code, which has been demonstrated in prior work to be related to correctness, we can reduce the number of patches that must be inspected to find the first correct patch. We evaluate this prioritization scheme over five APR tools, and find that we can reduce the number of patches that must be inspected in up to 70% of bugs and reduce the total number of patches inspected by up to two-thirds, paving the way for lower-cost program repair.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114244260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Madeline Endres, Pemma Reiter, S. Forrest, Westley Weimer
Over the past fifteen years, research on automated program repair has matured, and transitions to industry have begun. However, an impediment to wider adoption is concern over automatically gen-erated patch correctness. A review of 250 program repair research papers suggests that this concern can be addressed by adapting practices from modern code review, such as multiple anonymized reviews and checklists with well-defined terminology, to better evaluate the correctness and acceptability of plausible patches. In this paper, we argue that adopting such practices from modern code review for automated program repair research can increase developer trust, paving the way for wider industrial deployments.
{"title":"What Can Program Repair Learn From Code Review?","authors":"Madeline Endres, Pemma Reiter, S. Forrest, Westley Weimer","doi":"10.1145/3524459.3527352","DOIUrl":"https://doi.org/10.1145/3524459.3527352","url":null,"abstract":"Over the past fifteen years, research on automated program repair has matured, and transitions to industry have begun. However, an impediment to wider adoption is concern over automatically gen-erated patch correctness. A review of 250 program repair research papers suggests that this concern can be addressed by adapting practices from modern code review, such as multiple anonymized reviews and checklists with well-defined terminology, to better evaluate the correctness and acceptability of plausible patches. In this paper, we argue that adopting such practices from modern code review for automated program repair research can increase developer trust, paving the way for wider industrial deployments.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134055616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated program repair (APR) tools have promising results, but what are APR's limits? The answer could help researchers design tool trade-offs and manage user expectations. Since APR is undecidable, as are two of its typical phases, tools must use conservative approximations. Such approximations can help APR tools be better understood and can lead to a theory of sound APR.
{"title":"Be Realistic: Automated Program Repair is a Combination of Undecidable Problems","authors":"Amirfarhad Nilizadeh, Gary T. Leavens","doi":"10.1145/3524459.3527346","DOIUrl":"https://doi.org/10.1145/3524459.3527346","url":null,"abstract":"Automated program repair (APR) tools have promising results, but what are APR's limits? The answer could help researchers design tool trade-offs and manage user expectations. Since APR is undecidable, as are two of its typical phases, tools must use conservative approximations. Such approximations can help APR tools be better understood and can lead to a theory of sound APR.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133623441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The goal of Automated Program Repair (APR) is to find a fix to software bugs, without human intervention. The so-called Gener-ate and Validate (G&V) approach deemed to be the most popular method in the last few years, where the APR tool creates a patch and it is validated against an oracle. Recent years for Natural Language Processing (NLP) were of great interest, with new pre-trained models shattering records on tasks ranging from sentiment analysis to question answering. Usually these deep learning models inspire the APR community as well. These approaches usually require a large dataset on which the model can be trained (or fine-tuned) and evaluated. The criterion to accept a patch depends on the underlying dataset, but usually the generated patch should be exactly the same as the one created by a human developer. As NLP models are more and more capable to form sentences, and the sentences will form coherent paragraphs, the APR tools are also better and better at generating syntactically and semantically correct source code. As the Generative Pre-trained Transformer (GPT) model is now avail-able to everyone thanks to the NLP and AI research community, it can be fine-tuned to specific tasks (not necessarily on natural language). In this work we use the GPT-2 model to generate source code, to the best of our knowledge, the GPT-2 model was not used for Automated Program Repair so far. The model is fine-tuned for a specific task: it has been taught to fix JavaScript bugs automatically. To do so, we trained the model on 16863JS code snippets, where it could learn the nature of the observed programming language. In our experiments we observed that the GPT-2 model was able to learn how to write syntactically correct source code almost on every attempt, although it failed to learn good bug-fixes in some cases. Nonetheless it was able to generate the correct fixes in most of the cases, resulting in an overall accuracy up to 17.25%.
{"title":"Towards JavaScript program repair with Generative Pre-trained Transformer (GPT-2)","authors":"Márk Lajkó, Viktor Csuvik, László Vidács","doi":"10.1145/3524459.3527350","DOIUrl":"https://doi.org/10.1145/3524459.3527350","url":null,"abstract":"The goal of Automated Program Repair (APR) is to find a fix to software bugs, without human intervention. The so-called Gener-ate and Validate (G&V) approach deemed to be the most popular method in the last few years, where the APR tool creates a patch and it is validated against an oracle. Recent years for Natural Language Processing (NLP) were of great interest, with new pre-trained models shattering records on tasks ranging from sentiment analysis to question answering. Usually these deep learning models inspire the APR community as well. These approaches usually require a large dataset on which the model can be trained (or fine-tuned) and evaluated. The criterion to accept a patch depends on the underlying dataset, but usually the generated patch should be exactly the same as the one created by a human developer. As NLP models are more and more capable to form sentences, and the sentences will form coherent paragraphs, the APR tools are also better and better at generating syntactically and semantically correct source code. As the Generative Pre-trained Transformer (GPT) model is now avail-able to everyone thanks to the NLP and AI research community, it can be fine-tuned to specific tasks (not necessarily on natural language). In this work we use the GPT-2 model to generate source code, to the best of our knowledge, the GPT-2 model was not used for Automated Program Repair so far. The model is fine-tuned for a specific task: it has been taught to fix JavaScript bugs automatically. To do so, we trained the model on 16863JS code snippets, where it could learn the nature of the observed programming language. In our experiments we observed that the GPT-2 model was able to learn how to write syntactically correct source code almost on every attempt, although it failed to learn good bug-fixes in some cases. Nonetheless it was able to generate the correct fixes in most of the cases, resulting in an overall accuracy up to 17.25%.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114942182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
OpenAI's Codex, a GPT-3like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, two important tasks in automated program repair. Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state of the art techniques. Our results also show that Codex is more successful at repairing Python than Java, fixing 50% more bugs in Python.
{"title":"Can OpenAI's Codex Fix Bugs?: An evaluation on QuixBugs","authors":"Julian Aron Prenner, Hlib Babii, R. Robbes","doi":"10.1145/3524459.3527351","DOIUrl":"https://doi.org/10.1145/3524459.3527351","url":null,"abstract":"OpenAI's Codex, a GPT-3like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesizing code snippets that are syntactically and semantically valid in most cases. In this work, we want to investigate whether Codex is able to localize and fix bugs, two important tasks in automated program repair. Our initial evaluation uses the multi-language QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not being trained for APR, Codex is surprisingly effective, and competitive with recent state of the art techniques. Our results also show that Codex is more successful at repairing Python than Java, fixing 50% more bugs in Python.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127372629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spectrum-Based Fault Localization (SBFL) computes suspicion scores, using risk evaluation formulas, for program elements (e.g., state-ments, methods, or classes) by counting how often each element is executed or not executed by passing versus failing test cases. The elements are then ranked from most suspicious to least suspicious based on their scores. The elements with the highest scores are thought to be the most faulty. The final ranking list of program elements helps testers during the debugging process when attempting to locate the source of a bug in the program under test. In this paper, we present an approach that gives more importance to pro-gram elements that are executed by more failed test cases compared to other elements. In essence, we are emphasizing the failing test cases factor because there are comparably much less failing tests than passing ones. We multiply each element's suspicion score ob-tained by an SBFL formula by this importance weight, which is the ratio of covering failing tests over all failing tests. The proposed approach can be applied to SBFL formulas without modifying their structures. The experimental results of our study show that our approach achieved a better performance in terms of average ranking compared to the underlying SBFL formulas. It also improved the Top- N categories and increased the number of cases in which the faulty method became the top-ranked element.
{"title":"Enhancing Spectrum Based Fault localization Via Emphasizing Its Formulas With Importance Weight","authors":"Q. Sarhan","doi":"10.1145/3524459.3527349","DOIUrl":"https://doi.org/10.1145/3524459.3527349","url":null,"abstract":"Spectrum-Based Fault Localization (SBFL) computes suspicion scores, using risk evaluation formulas, for program elements (e.g., state-ments, methods, or classes) by counting how often each element is executed or not executed by passing versus failing test cases. The elements are then ranked from most suspicious to least suspicious based on their scores. The elements with the highest scores are thought to be the most faulty. The final ranking list of program elements helps testers during the debugging process when attempting to locate the source of a bug in the program under test. In this paper, we present an approach that gives more importance to pro-gram elements that are executed by more failed test cases compared to other elements. In essence, we are emphasizing the failing test cases factor because there are comparably much less failing tests than passing ones. We multiply each element's suspicion score ob-tained by an SBFL formula by this importance weight, which is the ratio of covering failing tests over all failing tests. The proposed approach can be applied to SBFL formulas without modifying their structures. The experimental results of our study show that our approach achieved a better performance in terms of average ranking compared to the underlying SBFL formulas. It also improved the Top- N categories and increased the number of cases in which the faulty method became the top-ranked element.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129509171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Defects4J is a popular dataset against which many Java Automatic Program Repair (APR) tools benchmark their performance. However, recent evidence suggests that some APR tools overfit to Defects4J, producing plausible patches which are incorrect. What we do not currently know is whether there is any commonality in the features of these plausible patches that turn out not to be correct. We compare the features of Defects4J's human written patches in terms of those correctly patched by existing APR tools and those incorrectly patched. We found that 48.4% of Defects4J v1.5 have been automatically patched by existing APR tools; of which only 28.9% have been correctly patched leaving 19.5% incorrectly patched. We found patches of defects that added a method call, added a variable, or wrapped existing code with new code, such as a try/catch block were significantly associated with incorrect patches. Editing only a single line was significantly associated with correct patches. Our results suggest that current tools are weak at generating multi-line patches and synthesising new code especially when wrapping existing code. Our results highlight potential future areas of development for new APR approaches, such as developing a tool that effectively repairs defects that require a try/catch block. Our replication Package is available online11Replication Package available at: https://github.com/IncorrectDefects/ReplicationPackage.
{"title":"Some Automatically Generated Patches are More Likely to be Correct than Others: An Analysis of Defects4J Patch Features","authors":"G. Bennett, T. Hall, David Bowes","doi":"10.1145/3524459.3527348","DOIUrl":"https://doi.org/10.1145/3524459.3527348","url":null,"abstract":"Defects4J is a popular dataset against which many Java Automatic Program Repair (APR) tools benchmark their performance. However, recent evidence suggests that some APR tools overfit to Defects4J, producing plausible patches which are incorrect. What we do not currently know is whether there is any commonality in the features of these plausible patches that turn out not to be correct. We compare the features of Defects4J's human written patches in terms of those correctly patched by existing APR tools and those incorrectly patched. We found that 48.4% of Defects4J v1.5 have been automatically patched by existing APR tools; of which only 28.9% have been correctly patched leaving 19.5% incorrectly patched. We found patches of defects that added a method call, added a variable, or wrapped existing code with new code, such as a try/catch block were significantly associated with incorrect patches. Editing only a single line was significantly associated with correct patches. Our results suggest that current tools are weak at generating multi-line patches and synthesising new code especially when wrapping existing code. Our results highlight potential future areas of development for new APR approaches, such as developing a tool that effectively repairs defects that require a try/catch block. Our replication Package is available online11Replication Package available at: https://github.com/IncorrectDefects/ReplicationPackage.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116126913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Test-based generate-and-validate automated program repair (APR) systems often generate plausible patches that pass the test suite without fixing the bug. So far, several approaches for automatic assessment of the APR-generated patches are proposed. Among them, dynamic patch correctness assessment relies on comparing run-time information obtained from the program before and after patching. Object similarity-based dynamic patch ranking approaches, specifically, capture system state snapshots after the impact point of patches and express behavior differences in term of object graphs similarities. Dynamic approaches rely on the assumption that, when running the originally passing test cases, the correct patches will not alter the program behavior in a significant way, but such patches will significantly change program behavior for the failing test cases. This paper presents the results of an extensive empirical study on two object similarity-based approaches, i.e., ObjSim and CIP, to rank 1,290 APR-generated patches, used in previous APR research. We found that although ObjSim outperforms CIP, in terms of the number of patches ranked in top-1 position, it still does not offer an improvement over random baseline ranking, representing the setting with no automatic patch correctness assessment in place. This observation warrants further research on the validity of the assumptions underlying these two techniques and the techniques based on similar assumptions.
{"title":"Revisiting Object Similarity-based Patch Ranking in Automated Program Repair: An Extensive Study","authors":"Ali Ghanbari","doi":"10.1145/3524459.3527354","DOIUrl":"https://doi.org/10.1145/3524459.3527354","url":null,"abstract":"Test-based generate-and-validate automated program repair (APR) systems often generate plausible patches that pass the test suite without fixing the bug. So far, several approaches for automatic assessment of the APR-generated patches are proposed. Among them, dynamic patch correctness assessment relies on comparing run-time information obtained from the program before and after patching. Object similarity-based dynamic patch ranking approaches, specifically, capture system state snapshots after the impact point of patches and express behavior differences in term of object graphs similarities. Dynamic approaches rely on the assumption that, when running the originally passing test cases, the correct patches will not alter the program behavior in a significant way, but such patches will significantly change program behavior for the failing test cases. This paper presents the results of an extensive empirical study on two object similarity-based approaches, i.e., ObjSim and CIP, to rank 1,290 APR-generated patches, used in previous APR research. We found that although ObjSim outperforms CIP, in terms of the number of patches ranked in top-1 position, it still does not offer an improvement over random baseline ranking, representing the setting with no automatic patch correctness assessment in place. This observation warrants further research on the validity of the assumptions underlying these two techniques and the techniques based on similar assumptions.","PeriodicalId":131481,"journal":{"name":"2022 IEEE/ACM International Workshop on Automated Program Repair (APR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126448478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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