Privacy issues in mobile apps have become a key concern of researchers, practitioners and users. We carried out a large-scale analysis of eHealth app user reviews to identify their key privacy concerns. We then analys...
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Extreme mutation testing (XMT) detects undesirable pseudo-testedness in a program by deleting the method bodies of covered code and observing whether the test suite can detect their absence. Even though XMT may identi...
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ISBN:
(纸本)9798350395693;9798350395686
Extreme mutation testing (XMT) detects undesirable pseudo-testedness in a program by deleting the method bodies of covered code and observing whether the test suite can detect their absence. Even though XMT may identify test limitations, its coarse granularity means that it may overlook testing inadequacies, particularly at the statement level, that developers may want to address before committing the resources demanded by traditional mutation testing. This paper proposes the use of the statement deletion mutation operator (SDL) to uncover pseudo-tested statements in addition to complete methods. In an experimental evaluation involving four frequently-studied, large, Apache Commons Java projects and 23 projects randomly selected from the Maven Central Repository, we found 722 different cases of pseudo-tested statements. Critically, we discovered that 48% of these statements exist outside of pseudo-tested methods, meaning that the detection of testing deficiencies related to these statements would normally be left to traditional, resource-intensive, mutation testing. Also, we found that a popular Java mutation testing tool would not have mutated some of the statement types involved in the first place, effectively rendering these issues, hitherto, hard to discover. This paper therefore demonstrates that XMT alone is insufficient and should be combined with pseudo-tested statement evaluation to pinpoint subtle, yet important, testing oversights that a developer should tackle before applying traditional mutation testing.
This paper describes a developed and tested complex method for automating the positioning system of an auto operator of a galvanic line using a two-channel inductive encoder, a frequency converter for motion control a...
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Artificial intelligence and big data computing are developing at a fast pace, which has resulted in the creation of several software service systems that use different machine learning models. By processing multimedia...
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Drawing identification is an essential step for welding automation and intelligence. The software for 3D data identification and analysis of workpiece drawings is based on the pipeline files generated by Revit softwar...
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The Building Information Modelling (BIM) methodology has been adopted in the construction industry, supporting the development of projects. Its implementation has improved the collaboration and integration in the desi...
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In a large-scale Continuous Integration (CI) environment, regression testing can encounter high time and resource demands in ad hoc execution. Therefore, Test Case Prioritization (TCP) is crucial for enhancing the reg...
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ISBN:
(纸本)9798350344806;9798350344790
In a large-scale Continuous Integration (CI) environment, regression testing can encounter high time and resource demands in ad hoc execution. Therefore, Test Case Prioritization (TCP) is crucial for enhancing the regression testing efficiency of CI. TCP methods aim to optimize regression testing by ordering test cases to effectively cover new code changes and their potential side effects and to maximize early fault detection. Traditional prioritization processes use diverse data sources, including code coverage analysis, test execution history, and domain-specific features. Heuristic-based or code-coverage-driven prioritization techniques may not be sufficient for accurate results in a rapidly changing environment. For this reason, there has been a significant shift towards employing Machine Learning (ML) techniques in TCP in recent years to harness the vast and complex datasets generated by CI practices. ML-based TCP approaches integrate multifaceted test case features from various sources to enhance the accuracy of test case prioritization. This trend reflects a broader movement towards data-driven decisionmaking in software testing, offering the potential to significantly reduce the regression testing burden by tailoring test suites more effectively to the needs of each software build, thereby saving time and resources while maintaining or improving the software quality. Recent studies have shown that the ML-based methods used in TCP can be categorized into four groups: Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Natural Language Processing. Codebases for software projects can be changed rapidly by introducing new feature distributions into the CI systems. We analyzed a Java application's CI and version control system (VCS) history data received from the international Business Machines Corporation (IBM) [1], [6], [7]. The frequent inclusion of new test suites introduced new patterns into the dataset properties. To keep up with these c
Big Data Analytics For Synchrophasors (BigSync) is an offline engineering analysis software solution developed at Indian Institute of Technology Palakkad. The software solution allows the user to do post-event analysi...
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The softwareengineering community recently has witnessed widespread deployment of AI programming assistants, such as GitHub Copilot. However, in practice, developers do not accept AI programming assistants’ initial ...
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In the decision system, the lower approximation set keeps expanding with adding features dynamically. But when enough features are added, the lower approximation set stabilizes. This provides a criterion for feature s...
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