Pub Date : 2023-10-13DOI: 10.1142/s0218194023500614
Guoqiang Yin, Wei Wang
{"title":"OdegVul: An Approach for Statement-Level Defect Prediction","authors":"Guoqiang Yin, Wei Wang","doi":"10.1142/s0218194023500614","DOIUrl":"https://doi.org/10.1142/s0218194023500614","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1142/s0218194023410024
Lixuan Li, Jie Li, Yihui Xu, Hao Zhu, Xiaofang Zhang
Code summarization is a task that aims at automatically producing descriptions of source code. Recently many deep-learning-based approaches have been proposed to generate accurate code summaries, among which pre-trained models (PTMs) for programming languages have achieved promising results. It is well known that source code written in programming languages is highly structured and unambiguous. Though previous work pre-trained the model with well-design tasks to learn universal representation from a large scale of data, they have not considered structure information during the fine-tuning stage. To make full use of both the pre-trained programming language model and the structure information of source code, we utilize Flow-Augmented Abstract Syntax Tree (FA-AST) of source code for structure information and propose GraphPLBART — Graph-augmented Programming Language and Bi-directional Auto-Regressive Transformer, which can effectively introduce structure information to a well PTM through a cross attention layer. Compared with the best-performing baselines, GraphPLBART still improves by 3.2%, 7.1%, and 1.2% in terms of BLEU, METEOR, and ROUGE-L, respectively, on Java dataset, and also improves by 4.0%, 6.3%, and 2.1% on Python dataset. Further experiment shows that the structure information from FA-AST has significant benefits for the performance of GraphPLBART. In addition, our meticulous manual evaluation experiment further reinforces the superiority of our proposed approach. This demonstrates its remarkable abstract quality and solidifies its position as a promising solution in the field of code summarization.
{"title":"Enhancing Code Summarization with Graph Embedding and Pre-trained Model","authors":"Lixuan Li, Jie Li, Yihui Xu, Hao Zhu, Xiaofang Zhang","doi":"10.1142/s0218194023410024","DOIUrl":"https://doi.org/10.1142/s0218194023410024","url":null,"abstract":"Code summarization is a task that aims at automatically producing descriptions of source code. Recently many deep-learning-based approaches have been proposed to generate accurate code summaries, among which pre-trained models (PTMs) for programming languages have achieved promising results. It is well known that source code written in programming languages is highly structured and unambiguous. Though previous work pre-trained the model with well-design tasks to learn universal representation from a large scale of data, they have not considered structure information during the fine-tuning stage. To make full use of both the pre-trained programming language model and the structure information of source code, we utilize Flow-Augmented Abstract Syntax Tree (FA-AST) of source code for structure information and propose GraphPLBART — Graph-augmented Programming Language and Bi-directional Auto-Regressive Transformer, which can effectively introduce structure information to a well PTM through a cross attention layer. Compared with the best-performing baselines, GraphPLBART still improves by 3.2%, 7.1%, and 1.2% in terms of BLEU, METEOR, and ROUGE-L, respectively, on Java dataset, and also improves by 4.0%, 6.3%, and 2.1% on Python dataset. Further experiment shows that the structure information from FA-AST has significant benefits for the performance of GraphPLBART. In addition, our meticulous manual evaluation experiment further reinforces the superiority of our proposed approach. This demonstrates its remarkable abstract quality and solidifies its position as a promising solution in the field of code summarization.","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1142/s0218194023500602
Cecilia Apa, Martin Solari, Diego Vallespir, G. Travassos
{"title":"Software Industry Perception of Technical Debt and its Management","authors":"Cecilia Apa, Martin Solari, Diego Vallespir, G. Travassos","doi":"10.1142/s0218194023500602","DOIUrl":"https://doi.org/10.1142/s0218194023500602","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"68 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139322094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-16DOI: 10.1142/s0218194023500493
Thanh Trong Vu, Thanh-Dat Do, Hieu Dinh Vo
Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to the lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilize either the changed codes or the changed codes combined with their surrounding statements. This paper proposes a method to represent code changes by combining the changed codes and the unchanged codes which have program dependence on the changed codes. Specifically, we first create program dependence graphs (PDGs) of source code before and after the change. After that, slices related to the changed code from these PDGs are extracted. These slices are then merged to represent the change. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4.
{"title":"Context-Encoded Code Change Representation for Automated Commit Message Generation","authors":"Thanh Trong Vu, Thanh-Dat Do, Hieu Dinh Vo","doi":"10.1142/s0218194023500493","DOIUrl":"https://doi.org/10.1142/s0218194023500493","url":null,"abstract":"Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to the lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilize either the changed codes or the changed codes combined with their surrounding statements. This paper proposes a method to represent code changes by combining the changed codes and the unchanged codes which have program dependence on the changed codes. Specifically, we first create program dependence graphs (PDGs) of source code before and after the change. After that, slices related to the changed code from these PDGs are extracted. These slices are then merged to represent the change. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4.","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135304614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1142/s0218194023500559
Shumei Wu, Zexing Chang, Zhanwen Zhang, Zheng Li, Y. Liu
{"title":"DTester: Diversity-driven Test Case Generation for Web Applications","authors":"Shumei Wu, Zexing Chang, Zhanwen Zhang, Zheng Li, Y. Liu","doi":"10.1142/s0218194023500559","DOIUrl":"https://doi.org/10.1142/s0218194023500559","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63936972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.1142/s021819402350050x
F. Belli, Tugkan Tuglular, Ekincan Ufuktepe
{"title":"Unifying Behavioral and Feature Modeling for Testing of Software Product Lines","authors":"F. Belli, Tugkan Tuglular, Ekincan Ufuktepe","doi":"10.1142/s021819402350050x","DOIUrl":"https://doi.org/10.1142/s021819402350050x","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63936929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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