CC2Vec

Hoang, Thong; Kang, Hong Jin; Lo, David; Lawall, Julia

doi:10.1145/3377811.3380361

Cited by 139 publications

(43 citation statements)

References 51 publications

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“…In contrast, if the representations miss some key information, it would be very hard, if not impossible, to obtain good results. Therefore, for code-change-related tasks, the quality of code change representations is critical to the effectiveness of automated approaches [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, many of them adopt task-specific architectures and are trained from scratch, which makes it non-trivial to adapt them to other tasks, especially the tasks with only small datasets. In addition, existing learning-based techniques either only focus on the changed code [3], [8], [16], separately encode the changed code and its context [14], [17], or encode the code change as a whole [2], [13], [18]. Some of them ignore the context or do not highlight the changed code.…”

Section: Introductionmentioning

confidence: 99%

“…Only a few studies focus on general approaches for learning code change representations that can be used in diverse tasks [8], [16]. Yin et al [16] proposed to learn distributed representations of small code edits by training an auto-encoder to reconstruct edits.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, their approach only focuses on small code edits (i.e., a single hunk with no more than 3 changed lines) while many software engineering tasks require encoding code changes with multi hunks [2], [3]. Hoang et al [8] proposed an approach named CC2Vec to learn code change representations that can be used to boost multiple codechange-related tasks. However, CC2Vec only considers the added and removed code lines and ignores their context.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to Yin et al's work [16], CCRep targets commit-level code changes, which are employed by plenty of common code-change-related tasks. Different from CC2Vec [8], our approach is jointly trained with task-specific components like classifiers on the target task, not requiring additional labels for supervision. Considering the limitations of existing task-specific learning-based techniques, first, CCRep adopts a pre-trained code model to learn the representations of the code before and after a change.…”

Section: Introductionmentioning

confidence: 99%

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CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

Liu¹,

Tang²,

Xia³

et al. 2023

Preprint

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Representing code changes as numeric feature vectors, i.e., code change representations, is usually an essential step to automate many software engineering tasks related to code changes, e.g., commit message generation and just-intime defect prediction. Intuitively, the quality of code change representations is crucial for the effectiveness of automated approaches. Prior work on code changes usually designs and evaluates code change representation approaches for a specific task, and little work has investigated code change encoders that can be used and jointly trained on various tasks. To fill this gap, this work proposes a novel Code Change Representation learning approach named CCRep, which can learn to encode code changes as feature vectors for diverse downstream tasks. Specifically, CCRep regards a code change as the combination of its before-change and after-change code, leverages a pretrained code model to obtain high-quality contextual embeddings of code, and uses a novel mechanism named query back to extract and encode the changed code fragments and make them explicitly interact with the whole code change. To evaluate CCRep and demonstrate its applicability to diverse code-change-related tasks, we apply it to three tasks: commit message generation, patch correctness assessment, and just-in-time defect prediction. Experimental results show that CCRep outperforms the state-ofthe-art techniques on each task.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

Liu¹,

Tang²,

Xia³

et al. 2023

Preprint

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Just‐in‐time identification for cross‐project correlated issues

Ren,

Li,

Chen

et al. 2023

J Software Evolu Process

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Issue tracking systems are now prevalent in software development, which would help developers submit and discuss issues to solve development problems on software projects. Most previous studies have been conducted to analyze issue relations within projects, such as recommending similar or duplicate bug issues. However, along with the popularization of co‐developing through multiple projects, many issues are cross‐project correlated (CPC), that is, one issue is associated with another issue in a different project. When developers meet with CPC issues, it may primarily increase the difficulties of solving them because they need information from not only their projects but also other related projects that developers are not familiar with. Identifying a CPC issue as early as possible is a fundamental challenge for both managers and developers to allocate the resources for software maintenance and estimate the effort to solve it. This paper proposes 11 issue metrics of two groups to describe textual summary and reporters' activity, which can be extracted just after the issue was reported. We employ these 11 issue metrics to construct just‐in‐time (JIT) prediction models to identify CPC issues. To evaluate the effect of CPC issue prediction models, we conduct experiments on 16 open‐source data science and deep learning projects and compare our prediction model with two baseline models based on textual features (i.e., Term Frequency‐Inverse Document Frequency [TF‐IDF] and Word Embedding), which are commonly adopted by previous studies on issue prediction. The results show that the JIT prediction model based on issue metrics has significantly improved the performance of CPC issue prediction under two evaluation indicators, Matthew's correlation coefficient (MCC) and F1. In addition, we find that the prediction model is more suitable for large‐scale complex core projects in the open‐source ecosystem.

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Regression test prioritization leveraging source code similarity with tree kernels

Altiero,

Corazza,

Di Martino

et al. 2024

J Software Evolu Process

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Regression test prioritization (RTP) is an active research field, aiming at re‐ordering the tests in a test suite to maximize the rate at which faults are detected. A number of RTP strategies have been proposed, leveraging different factors to reorder tests. Some techniques include an analysis of changed source code, to assign higher priority to tests stressing modified parts of the codebase. Still, most of these change‐based solutions focus on simple text‐level comparisons among versions. We believe that measuring source code changes in a more refined way, capable of discriminating between mere textual changes (e.g., renaming of a local variable) and more structural changes (e.g., changes in the control flow), could lead to significant benefits in RTP, under the assumption that major structural changes are also more likely to introduce faults. To this end, we propose two novel RTP techniques that leverage tree kernels (TK), a class of similarity functions largely used in Natural Language Processing on tree‐structured data. In particular, we apply TKs to abstract syntax trees of source code, to more precisely quantify the extent of structural changes in the source code, and prioritize tests accordingly. We assessed the effectiveness of the proposals by conducting an empirical study on five real‐world Java projects, also used in a number of RTP‐related papers. We automatically generated, for each considered pair of software versions (i.e., old version, new version) in the evolution of the involved projects, 100 variations with artificially injected faults, leading to over 5k different software evolution scenarios overall. We compared the proposed prioritization approaches against well‐known prioritization techniques, evaluating both their effectiveness and their execution times. Our findings show that leveraging more refined code change analysis techniques to quantify the extent of changes in source code can lead to relevant improvements in prioritization effectiveness, while typically introducing negligible overheads due to their execution.

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CC2Vec

Cited by 139 publications

References 51 publications

CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

Just‐in‐time identification for cross‐project correlated issues

Regression test prioritization leveraging source code similarity with tree kernels

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