Empirical Investigations of Large-Scale Regression Testing

回归测试是现代软件开发实践中一个重要但昂贵的部分。该项目的中心假设是，在当前的研究中，几个关键问题没有得到充分的考虑，这些问题可以被单独或组合地利用来控制回归测试过程。如果是真的，那么成功的研究将导致更便宜、更快、更可预测和更有效的回归测试过程，从而在整个行业节省大量的时间和金钱。因此，该项目进行了以下实验：1.比较不同的RTS技术，并解释它们的输入特征如何影响它们的性能。2.测量不同的应用策略(触发回归测试的规则)对RTS性能的影响。3.使用实验1和实验2中的信息，开发和评估数据驱动(基于每个测试用例的先前性能)的技术，以确定测试用例执行的优先顺序，确定集成更改的顺序，以及修剪和优化原始测试套件。4.探索这些技术是否以及如何组合在一起，以及这会产生什么影响；5.在大型软件系统的在线长期研究中验证这些模型、技术和启发式方法。

Regression testing is an important, but expensive part of modernsoftware development practices. Central hypotheses of the project are that several key issues have not been adequately considered in current research and that these issues can be exploited, singly and incombination, to control the regression testing process. If true, thensuccessful research will lead to cheaper, faster, more predictable andmore effective regression testing processes, thereby saving a greatdeal of time and money throughout the industry. Consequently, the project conducts the following experiments: 1. Compare different RTS techniques and to explain how features of their inputs affect their performance. 2. Measure how different application policies (rules that trigger regression testing) affect RTS performance. 3. Develop and evaluate, using the information from experiments 1 and 2, data-driven (based on each test case's prior performance)techniques for prioritizing test case execution, for determining theorder in which changes are integrated, and for pruning and optimizingthe original test suites. 4. Explore whether and how these techniques can be combined and what effect that has, and 5. Validate these models, techniques, and heuristics in an on-line, long-term study of a large software system.