ERD-CQC : Enhanced Rule and Dependency Code Quality Check for Java
Authors: 
Yi Hou, Wuxia Jin, Zhijun Wang, Liuming Wang, Shuguang Chen, Yihan Wang, Lei Sang, Haijun Wang, Ting LiuAuthors Info & Claims
Pages 377 - 386
Abstract
In the field of software development, the application of code quality check tools has become a key factor in improving product quality and development efficiency. While many existing tools are effective at detecting common problems in code, there are still some limitations. Firstly, these tools rely on predefined rules that may not fully encompass real-world coding challenges. Secondly, a lack of consideration of dependencies leads to failure to report violations occurring across files or modules. Third, the metrics used by these tools primarily focus on object-oriented programming, limiting their ability to assess software quality from the perspective of nationalized standards. To address these issues, this work proposes a dependency-enhanced method namely ERD-CQC for code quality detection and measurement. ERD-CQC provides 88 detection rules and 45 metrics, supplementing checking rules in categories such as Circuit Breaking, Serializable, and Security. ERD-CQC constructs an infused graph by integrating abstract syntax trees (ASTs), entities, and dependencies for violation detection. Based on the detection results, ERD-CQC provides a code quality measurement system with 4 nationalized standard dimensions for the purpose of measuring code quality from multiple perspectives. To validate the effectiveness of ERD-CQC, we manually examined 647 compliant and 528 non-compliant code snippets. ERD-CQC achieves the recall and F1 score exceeding 98%. We also collected open-source projects and closed-source projects in the real world, containing a total of 4,319 non-compliant code snippets. On this real-world benchmark, the average F1 score of ERD-CQC is 11.44% higher than the advanced tool SonarQube. Finally, we visualized the quality measurement results based on metrics and found that open-source and closed-source projects have certain patterns in metric performance. Our work will benefit developers in checking, evaluating, and monitoring their software quality comprehensively.
References
[1]
Jarallah S Alghamdi, Raimi A Rufai, and Sohel M Khan. 2005. OOMeter: A software quality assurance tool. In Ninth European Conference on Software Maintenance and Reengineering. IEEE Computer Society, 190–191.
[2]
Midya Alqaradaghi, Gregory Morse, and Tamás Kozsik. 2021. Detecting security vulnerabilities with static analysis–A case study. Pollack Periodica (2021).
[3]
Luca Ardito, Riccardo Coppola, Luca Barbato, and Diego Verga. 2020. A tool-based perspective on software code maintainability metrics: a systematic literature review. Scientific Programming 2020 (2020), 1–26.
[4]
Carliss Y Baldwin and Kim B Clark. 2000. Design rules, Volume 1: The power of modularity. MIT press.
[5]
[5] Checkstyle.https://checkstyle.org
[6]
Shyam R Chidamber and Chris F Kemerer. 1994. A metrics suite for object oriented design. IEEE Transactions on software engineering 20, 6 (1994), 476–493.
[7]
[7] ERD-CQC.https://github.com/ERD-CQC/ERD-CQC
[8]
Gang Fan, Xiaoheng Xie, Xunjin Zheng, Yinan Liang, and Peng Di. 2023. Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents. arXiv preprint arXiv:2310.08837 (2023).
[9]
Hongzhou Fang, Yuanfang Cai, Rick Kazman, and Jason Lefever. 2022. Cider: concept-based interactive design recovery. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings. 26–30.
[10]
Daniel Galin. 2004. Software quality assurance: from theory to implementation. Pearson education.
[11]
Giovanni Grano, Fabio Palomba, and Harald C Gall. 2019. Lightweight assessment of test-case effectiveness using source-code-quality indicators. IEEE Transactions on Software Engineering 47, 4 (2019), 758–774.
[12]
Sebastian Hönel, Morgan Ericsson, Welf Löwe, and Anna Wingkvist. 2023. Metrics As Scores: A Tool- and Analysis Suite and Interactive Application for Exploring Context-Dependent Distributions. Journal of Open Source Software 8, 88 (2023), 4913. https://doi.org/10.21105/joss.04913
[13]
[13] Infer.https://github.com/facebook/infer
[14]
[14] ISO/IEC 5055.https://www.iso.org/standard/80623.html
[15]
Wuxia Jin, Yuanfang Cai, Rick Kazman, Qinghua Zheng, Di Cui, and Ting Liu. 2019. Enre: a tool framework for extensible entity relation extraction. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 67–70.
[16]
Wuxia Jin, Shuo Xu, Dawei Chen, Jiajun He, Dinghong Zhong, Ming Fan, Hongxu Chen, Huijia Zhang, and Ting Liu. 2024. PyAnalyzer: An Effective and Practical Approach for Dependency Extraction from Python Code. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE). IEEE Computer Society, 896–896.
[17]
Wuxia Jin, Yuyun Zhang, Jiaowei Shang, Yi Hou, Ming Fan, and Ting Liu. 2023. Identifying Code Changes for Architecture Decay via a Metric Forest Structure. In 2023 ACM/IEEE International Conference on Technical Debt (TechDebt). IEEE, 62–71.
[18]
Wuxia Jin, Dinghong Zhong, Yuanfang Cai, Rick Kazman, and Ting Liu. 2023. Evaluating the Impact of Possible Dependencies on Architecture-Level Maintainability. IEEE Transactions on Software Engineering 49, 3 (2023), 1064–1085. https://doi.org/10.1109/TSE.2022.3171288
[19]
Valentina Lenarduzzi, Fabiano Pecorelli, Nyyti Saarimaki, Savanna Lujan, and Fabio Palomba. 2023. A critical comparison on six static analysis tools: Detection, agreement, and precision. Journal of Systems and Software 198 (2023), 111575.
[20]
Zengyang Li, Paris Avgeriou, and Peng Liang. 2015. A systematic mapping study on technical debt and its management. Journal of Systems and Software 101 (2015), 193–220.
[21]
Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, 2022. Automating code review activities by large-scale pre-training. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1035–1047.
[22]
Han Liu, Sen Chen, Ruitao Feng, Chengwei Liu, Kaixuan Li, Zhengzi Xu, Liming Nie, Yang Liu, and Yixiang Chen. 2023. A comprehensive study on quality assurance tools for java. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. 285–297.
[23]
Alan MacCormack, John Rusnak, and Carliss Y Baldwin. 2006. Exploring the structure of complex software designs: An empirical study of open source and proprietary code. Management Science 52, 7 (2006), 1015–1030.
[24]
Diego Marcilio, Carlo A Furia, Rodrigo Bonifácio, and Gustavo Pinto. 2020. SpongeBugs: Automatically generating fix suggestions in response to static code analysis warnings. Journal of Systems and Software 168 (2020), 110671.
[25]
Thomas J McCabe. 1976. A complexity measure. IEEE Transactions on software Engineering4 (1976), 308–320.
[26]
Ran Mo, Yuanfang Cai, Rick Kazman, Lu Xiao, and Qiong Feng. 2016. Decoupling level: A new metric for architectural maintenance complexity. In Proceedings of the 38th International Conference on Software Engineering. 499–510.
[27]
Haris Mumtaz, Paramvir Singh, and Kelly Blincoe. 2023. Identifying refactoring opportunities for large packages by analyzing maintainability characteristics in Java OSS. Journal of Systems and Software (Aug 2023), 111717. https://doi.org/10.1016/j.jss.2023.111717
[28]
[28] P3C, Alibaba Group.https://github.com/alibaba/p3c
[29]
Weifeng Pan, Xin Du, Hua Ming, Dae-Kyoo Kim, and Zijiang Yang. 2023. Identifying Key Classes for Initial Software Comprehension: Can We Do It Better?. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 1878–1889.
[30]
Xin Peng, Chenxi Zhang, Zhongyuan Zhao, Akasaka Isami, Xiaofeng Guo, and Yunna Cui. 2022. Trace analysis based microservice architecture measurement. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1589–1599.
[31]
[31] PMD.https://pmd.github.io
[32]
Dan Port, Bill Taber, and LiQuo Huang. 2023. Investigating a NASA Cyclomatic Complexity Policy on Maintenance Risk of a Critical System. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 211–221.
[33]
Michael Pradel and Koushik Sen. 2018. Deepbugs: A learning approach to name-based bug detection. Proceedings of the ACM on Programming Languages 2, OOPSLA (2018), 1–25.
[34]
Srinivasan Sengamedu and Hangqi Zhao. 2022. Neural language models for code quality identification. In Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation. 5–10.
[35]
[35] SonarQube.https://github.com/SonarSource/sonarqube
[36]
[36] Spotbugs.https://spotbugs.github.io
[37]
Eldar Sultanow, André Ullrich, Stefan Konopik, and Gergana Vladova. 2018. Machine learning based static code analysis for software quality assurance. In 2018 Thirteenth International Conference on Digital Information Management (ICDIM). IEEE, 156–161.
[38]
Ricardo Terra, Luis Fernando Miranda, Marco Tulio Valente, and Roberto S Bigonha. 2013. Qualitas. class Corpus: A compiled version of the Qualitas Corpus. ACM SIGSOFT Software Engineering Notes 38, 5 (2013), 1–4.
[39]
Vishvajeet Thakur, Marouane Kessentini, and Tushar Sharma. 2020. Qscored: An open platform for code quality ranking and visualization. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 818–821.
[40]
Patanamon Thongtanunam, Chanathip Pornprasit, and Chakkrit Tantithamthavorn. 2022. Autotransform: Automated code transformation to support modern code review process. In Proceedings of the 44th international conference on software engineering. 237–248.
[41]
Michele Tufano, Fabio Palomba, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, Andrea De Lucia, and Denys Poshyvanyk. 2017. There and back again: Can you compile that snapshot?Journal of Software: Evolution and Process 29, 4 (2017), e1838.
[42]
Rosalia Tufano, Luca Pascarella, Michele Tufano, Denys Poshyvanyk, and Gabriele Bavota. 2021. Towards automating code review activities. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 163–174.
[43]
[43] Understand.https://scitools.com/
Index Terms
- ERD-CQC : Enhanced Rule and Dependency Code Quality Check for Java
Recommendations
Impact of refactoring on quality code evaluation
WRT '11: Proceedings of the 4th Workshop on Refactoring ToolsCode smells are characteristics of the software that may indicate a code or design problem that can make software hard to understand, to evolve and maintain. Detecting code smells in the code and consequently applying the right refactoring steps, when ...
Understanding and Detecting Harmful Code
SBES '20: Proceedings of the XXXIV Brazilian Symposium on Software EngineeringCode smells typically indicate poor design implementation and choices that may degrade software quality. Hence, they need to be carefully detected to avoid such poor design. In this context, some studies try to understand the impact of code smells on the ...
Does source code quality reflect the ratings of apps?
MOBILESoft '18: Proceedings of the 5th International Conference on Mobile Software Engineering and SystemsIn the past, bad code quality has been associated with higher bugproneness. At the same time, the main reason why mobile users negatively rate an app is due to the presence of bugs leading to crashes. In this paper, we preliminarily investigate the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2024
518 pages
ISBN:9798400707056
DOI:10.1145/3671016
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 July 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- Young Talent Fund of Association for Science and Technology in Shaanxi, China
- National Key R&D Program of China
Conference
Internetware 2024
Sponsor:
Acceptance Rates
Overall Acceptance Rate 55 of 111 submissions, 50%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)59
- Downloads (Last 6 weeks)8
Reflects downloads up to 27 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format