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P2 MOTSD: a multi-objective test selection tool using test suite diagnosability

MOTSD：一种使用测试套件可诊断性的多目标测试选择工具
FSE Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering2019匹配度 1/10 · 该论文研究多目标回归测试选择，使用粒子群优化与可诊断性度量，属于软件测试/软件工程领域；与用户关注的静态分析、LLM/Agent、浮点程序分析、贝叶斯程序分析等方向没有直接交叉。引用 9 inbox
Daniel Correia, Rui Abreu, Pedro Santos, João Nadkarni
测试、调试与修复
crossref: 2026-09-12

AI 速览

一句话：提出多目标测试选择工具 MOTSD，结合测试套件可诊断性与历史度量，用粒子群优化为 C# 大型系统加速回归反馈。

大型软件系统每次变更都运行完整回归测试代价过高，OutSystems 开发者需要更快、更早的反馈。本文提出并实现 MOTSD，一个面向 C# 代码库的多目标测试选择工具。该方法以测试套件可诊断性和历史度量为优化目标，利用粒子群优化算法选择测试用例子集，从而在有限测试预算下尽可能保留故障检测与诊断能力。论文报告了工具在工业环境中的实现挑战、当前实验结果和局限性；摘要未披露具体性能提升数值。

问题
大型系统回归测试中，每次变更后运行全部测试耗时过长，需更快、更早地向开发者反馈。
方法
MOTSD 在 C# 代码库中实现多目标测试选择，以测试套件可诊断性和历史度量为目标，并由粒子群优化算法驱动。
评估
在工业环境中报告实现挑战、当前实验结果和工具局限性；摘要未给出具体量化评估结果。
原文摘要（Abstract）

Performing regression testing on large software systems becomes unfeasible as it takes too long to run all the test cases every time a change is made. The main motivation of this work was to provide a faster and earlier feedback loop to the developers at OutSystems when a change is made. The developed tool, MOTSD, implements a multi-objective test selection approach in a C# code base using a test suite diagnosability metric and historical metrics as objectives and it is powered by a particle swarm optimization algorithm. We present implementation challenges, current experimental results and limitations of the tool when applied in an industrial context. Screencast demo link: https://www.youtube.com/watch?v=CYMfQTUu2BE

链接与引用

DOI 原文 ·

BibTeX 
@inproceedings{paperbot5132,
  title = {MOTSD: a multi-objective test selection tool using test suite diagnosability},
  author = {Daniel Correia and Rui Abreu and Pedro Santos and João Nadkarni},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3341187}
}