In software engineering, impact analysis consists in predicting the software elements (e.g. modules, classes, methods) potentially impacted by a change in the source code. Impact analysis is required to optimize the testing effort. In this paper, we propose a framework to predict error propagation. Based on 10 open-source Java projects and 5 classical mutation operators, we create 17000 mutants and study how the error they introduce propagates. This framework enables us to analyze impact prediction based on four types of call graph. Our results show that the sophistication indeed increases completeness of impact prediction. However, and surprisingly to us, the most basic call graph gives the highest trade-off between precision and recall for impact prediction.
Do and Rothermel @cite_17 describe a protocol to study test case prioritization techniques based on mutation. Their protocol and ours share the same idea, that of using test cases to determine which test cases are impacted by the change. However, we have a different goal: they study test case prioritization whereas we study impact prediction.
@cite_17
2017946300 2107940771 2136997225 1984494831
Regression test prioritization techniques re-order the execution of a test suite in an attempt to ensure that defects are revealed earlier in the test execution phase. In prior work, test suites were prioritized with respect to their ability to satisfy control flow-based and mutation-based test adequacy criteria. In this paper, we propose an approach to regression test prioritization that leverages the all-DUs test adequacy criterion that focuses on the definition and use of variables within the program under test. Our prioritization scheme is motivated by empirical studies that have shown that (i) tests fulfilling the all-DUs test adequacy criteria are more likely to reveal defects than those that meet the control flow-based criteria, (ii) there is an unclear relationship between all-DUs and mutation-based criteria, and (iii) mutation-based testing is significantly more expensive than testing that relies upon all-DUs.In support of our prioritization technique, we provide a formal statement of the algorithms and equations that we use to instrument the program under test, perform test suite coverage monitoring, and calculate test adequacy. Furthermore, we examine the architecture of a tool that implements our novel prioritization scheme and facilitates experimentation. The use of this tool in a preliminary experimental evaluation indicates that, for three case study applications, our prioritization can be performed with acceptable time and space overheads. Finally, these experiments also demonstrate that the prioritized test suite can have an improved potential to identify defects earlier during the process of test execution. Techniques for test-case prioritization re-order test cases to increase their rate of fault detection. When there is a fixed time budget that does not allow the execution of all the test cases, time-aware techniques for test-case prioritization may achieve a better rate of fault detection than traditional techniques for test-case prioritization. In this paper, we propose a novel approach to time-aware test-case prioritization using integer linear programming. To evaluate our approach, we performed experiments on two subject programs involving four techniques for our approach, two techniques for an approach to time-aware test-case prioritization based on genetic algorithms, and four traditional techniques for test-case prioritization. The empirical results indicate that two of our techniques outperform all the other techniques for the two subjects under the scenarios of both general and version-specific prioritization. The empirical results also indicate that some traditional techniques with lower analysis time cost for test-case prioritization may still perform competitively when the time budget is not quite tight. Test case prioritization techniques involve scheduling test cases for regression testing in an order that increases their effectiveness at meeting some performance goal. This is inefficient to re execute all the test cases in regression testing following the software modifications. Using information obtained from previous test case execution, prioritization techniques order the test cases for regression testing so that most beneficial are executed first thus allows an improved effectiveness of testing. One performance goal, rate of dependency detected among faults, measures how quickly dependency among faults are detected within the regression testing process. An improved rate of fault dependency can provide faster feedback on software and let developers start debugging on the severe faults that cause other faults to appear later. This paper presents the new metric for assessing rate of fault dependency detection and an algorithm to prioritize test cases. Using the new metric the effectiveness of this prioritization is shown comparing it with non-prioritized test case. Analysis proves that prioritized test cases are more effective in detecting dependency among faults. Test case prioritization involves scheduling test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also that detects the most severe faults at the earliest in its testing life cycle. In this paper, we propose to put forth a model for system level test case prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system test cases based on the six factors: customer priority, changes in requirement, implementation complexity, completeness, traceability and fault impact. The proposed prioritization technique is validated with two different validation techniques and is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.