Reflection-aware static regression test selection

Author:

Shi August1,Hadzi-Tanovic Milica1,Zhang Lingming2,Marinov Darko1,Legunsen Owolabi1

Affiliation:

1. University of Illinois at Urbana-Champaign, USA

2. University of Texas at Dallas, USA

Abstract

Regression test selection (RTS) aims to speed up regression testing by rerunning only tests that are affected by code changes. RTS can be performed using static or dynamic analysis techniques. Our prior study showed that static and dynamic RTS perform similarly for medium-sized Java projects. However, the results of that prior study also showed that static RTS can be unsafe, missing to select tests that dynamic RTS selects, and that reflection was the only cause of unsafety observed among the evaluated projects. In this paper, we investigate five techniques—three purely static techniques and two hybrid static-dynamic techniques—that aim to make static RTS safe with respect to reflection. We implement these reflection-aware (RA) techniques by extending the reflection-unaware (RU) class-level static RTS technique in a tool called STARTS. To evaluate these RA techniques, we compare their end-to-end times with RU, and with RetestAll, which reruns all tests after every code change. We also compare safety and precision of the RA techniques with Ekstazi, a state-of-the-art dynamic RTS technique; precision is a measure of unaffected tests selected. Our evaluation on 1173 versions of 24 open-source Java projects shows negative results. The RA techniques improve the safety of RU but at very high costs. The purely static techniques are safe in our experiments but decrease the precision of RU, with end-to-end time at best 85.8% of RetestAll time, versus 69.1% for RU. One hybrid static-dynamic technique improves the safety of RU but at high cost, with end-to-end time that is 91.2% of RetestAll. The other hybrid static-dynamic technique provides better precision, is safer than RU, and incurs lower end-to-end time—75.8% of RetestAll, but it can still be unsafe in the presence of test-order dependencies. Our study highlights the challenges involved in making static RTS safe with respect to reflection.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,Software

Reference75 articles.

1. Apache Software Foundation. 2019a. Apache Camel. (2019). http://camel.apache.org/ . Apache Software Foundation. 2019a. Apache Camel. (2019). http://camel.apache.org/ .

2. Apache Software Foundation. 2019b. Apache Commons Math. (2019). https://commons.apache.org/proper/commons-math/ . Apache Software Foundation. 2019b. Apache Commons Math. (2019). https://commons.apache.org/proper/commons-math/ .

3. Linda Badri Mourad Badri and Daniel St-Yves. 2005. Supporting predictive change impact analysis: A control call graph based technique. In APSEC. 167–175. Linda Badri Mourad Badri and Daniel St-Yves. 2005. Supporting predictive change impact analysis: A control call graph based technique. In APSEC. 167–175.

Cited by 20 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Approach to Regression Testing Selection based on Code Changes and Smells;8th Brazilian Symposium on Systematic and Automated Software Testing;2023-09-25

2. Optimizing Continuous Development by Detecting and Preventing Unnecessary Content Generation;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

3. Python Security in DevOps: Best Practices for Secure Coding, Configuration Management, and Continuous Testing and Monitoring;2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC);2023-07-06

4. An information retrieval-based regression test selection technique;Iran Journal of Computer Science;2023-05-15

5. Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective;2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE);2023-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3