DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery

Xie, Rui; Chen, Long; Ye, Wei; Li, Zhiyu; Hu, Tianxiang; Du, Dongdong; Zhang, Shikun

doi:10.1109/saner.2019.8667969

Cited by 25 publications

(26 citation statements)

References 18 publications

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“…Deeplink followed the process in order to calculate the semantic and code similarity, which includes data construction, generation of code embeddings, similarity calculation, and feature extraction. The result is supported from [30] by the experiment performed on six projects, which answered the research questions relying on the effectiveness of deeplink in order to recover the missing links, effects of code context, and semantics of deeplink providing 90of F1-measure.…”

Section: Deeplink: Issue-commit Link Recoverymentioning

confidence: 52%

“…Existing systems for issue commit link recovery extracts the features from issue report and commit log but it sometimes results in loss of semantics. Xie and Rui et al [30] proposed the design of a software that captures the semantics of code and issue-related text. Furthermore, it also calculates the semantics' similarity and code similarity by using support vector machine (SVM) classification.…”

Section: Deeplink: Issue-commit Link Recoverymentioning

confidence: 99%

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Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

et al. 2021

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Understanding how developers refactor their code is critical to support the design improvement process of software. This paper investigates to what extent code metrics are good indicators for predicting refactoring activity in the source code. In order to perform this, we formulated the prediction of refactoring operation types as a multi-class classification problem. Our solution relies on measuring metrics extracted from committed code changes in order to extract the corresponding features (i.e., metric variations) that better represent each class (i.e., refactoring type) in order to automatically predict, for a given commit, the method-level type of refactoring being applied, namely Move Method, Rename Method, Extract Method, Inline Method, Pull-up Method, and Push-down Method. We compared various classifiers, in terms of their prediction performance, using a dataset of 5004 commits and extracted 800 Java projects. Our main findings show that the random forest model trained with code metrics resulted in the best average accuracy of 75%. However, we detected a variation in the results per class, which means that some refactoring types are harder to detect than others.

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Section: Deeplink: Issue-commit Link Recoverymentioning

confidence: 52%

Section: Deeplink: Issue-commit Link Recoverymentioning

confidence: 99%

Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

et al. 2021

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“…The authors implemented a learning-to-rank model (Gradient Decision Tree) that was leveraged to re-rank the obtained results, thereby attaining much relative search results. Constructing KGs to improve software engineering practices and internal processes were also addressed in [100][101][102][103].…”

Section: Ictmentioning

confidence: 99%

Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

246

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“…Harer et al [31] use word2vec to generate word embedding for C/C++ tokens for software vulnerability prediction.The token embedding is used to initialize a TextCNN model for classification. Xie et al [32] and Zhang et al [33] build a code knowledge graph and learn code context representation based on this knowledge graph. The learned representations are used to recover the missing links between issues and commits.…”

Section: Code Representationmentioning

confidence: 99%

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Xie

Sun

et al. 2021

Preprint

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Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relationship between method code summaries and method names. More specifically, since a method's name can be considered as a shorter version of its code summary, we first introduce the tasks of generation and informativeness prediction of method names as two auxiliary training objectives for code summarization. A novel two-pass deliberation mechanism is then incorporated into our MTL architecture to generate more consistent intermediate states fed into a summary decoder, especially when informative method names do not exist. To evaluate our deliberation MTL approach, we carried out a large-scale experiment on two existing datasets for Java and Python. The experiment results show that our technique can be easily applied to many state-of-the-art neural models for code summarization and improve their performance. Meanwhile, our approach shows significant superiority when generating summaries for methods with non-informative names.

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DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery

Cited by 25 publications

References 18 publications

Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

Domain-specific knowledge graphs: A survey

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

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