A survey on Test Suite Reduction frameworks and tools <s> Comparison of Automated Fault-Localization based TSR Tools <s> Test-suite reduction technique aims to find a subset of the test suite while still satisfying the original test requirements; therefore, it can save the cost of software testing. Because many test cases have been removed, the testing information is also lost. Fault localization is a technique using testing information to locate the fault and widely used by programmers to debug programs, so it suffers from the side effects of test-suite reduction. How to reduce the test-suite size in software testing with the premise of retaining or improving fault-localization effectiveness has become the hot spot in the area of software debugging recently. In this paper, we propose a new approach that selectively keeps the limited redundant test cases in the reduced set; it makes the new reduced set relatively redundant compared to the original one, and we expect that it retains or even improves fault-localization effectiveness. We also describe a framework that implements our approach and conduct a set of empirical studies for evaluation. The results show that our approach can retain or even improve fault-localization effectiveness as expected. <s> BIB001 </s> A survey on Test Suite Reduction frameworks and tools <s> Comparison of Automated Fault-Localization based TSR Tools <s> A program fails. What now? Taking a single failing run, we record and minimize the interaction between objects to the set of calls relevant for the failure. The result is a minimal unit test that faithfully reproduces the failure at will: "Out of these 14,628 calls, only 2 are required". In a study of 17 real-life bugs, our JINSI prototype reduced the search space to 13.7% of the dynamic slice or 0.22% of the source code, with only 1--12 calls left to examine. <s> BIB002 </s> A survey on Test Suite Reduction frameworks and tools <s> Comparison of Automated Fault-Localization based TSR Tools <s> In order to improve the effectiveness of fault localization, researchers are interested in test-suite reduction to provide suitable test-suite inputs. Different test-suite reduction approaches have been proposed. However, the results are usually not ideal. Reducing the test-suite improperly or excessively can even negatively affect fault-localization effectiveness. In this paper, we propose a two-step test-suite reduction approach to remove the test cases which have little or no effect on fault localization, and improve the distribution evenness of concrete execution paths of test cases. This approach consists of coverage matrix based reduction and path vector based reduction, so it analyzes not only the test cases coverage but also the concrete path information. We design and implement experiments to verify the effect of our approach. The experimental results show that our reduced test-suite can improve fault-localization effectiveness. On average, our approach can reduce the size of a test-suite in 47.87% (for Siemens programs) and 23.03% (for space program). At the same time, on average our approach can improve the fault-localization effectiveness, 2.12 on Siemens programs and 0.13 on space program by Tarantula approach. <s> BIB003
The researchers have concluded that faults usually reside in execution paths of failed test cases BIB003 . Thus, the statements in failed test cases' paths might be more beneficial in improving fault-localization effectiveness. However, SrTC BIB001 only consider the coverage information of test cases instead of concrete path information. Consequently, SrTC BIB001 ) may remove the test cases which are relevant to faultlocalization requirements and ultimately achieves less fault-localization effectiveness. In contrast, JINSI BIB002 performs better in terms of improving fault-localization effectiveness, since it minimizes failing test cases based on call/return traces. Ultimately, JINSI BIB002 has the high potential to determine the significant number of faults than SrTC BIB001 . In comparison to SrTC BIB001 and JINSI BIB002 , GZoltar can speed-up the fault-localization process, since it provides different visual representations of the generated diagnostic report.