Oracle Assessment, Improvement and Placement

The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. This thesis analyses the prevalence of failed error propagation in programs with real faults to address the oracle placement problem and introduces an approach for iterative assessment and improvement of the oracles. To analyse failed error propagation in programs with real faults, we have conducted an empirical study, considering Defects4J, a benchmark of Java programs, of which we used all 6 projects available, 384 real bugs and 528 methods fixed to correct such bugs. The results indicate that the prevalence of failed error propagation is negligible. Moreover, the results on real faults differ from the results on mutants, indicating that if failed error propagation is taken into account, mutants are not a good surrogate of real faults. When measuring failed error propagation, for each method we use the strongest possible oracle as postcondition, which checks all externally observable program variables. The low prevalence of failed error propagation is caused by the presence of such a strong oracle, which usually is not available in practice. Therefore, there is a need for a technique to assess and improve existing weaker oracles. We propose a technique for assessing and improving test oracles, which necessarily places the human tester in the loop and is based on reducing the incidence of both false positives and false negatives. A proof showing that this approach results in an increase in the mutual information between the actual and perfect oracles is provided. The application of the approach to five real-world subjects shows that the fault detection rate of the oracles after improvement increases, on average, by 48.6%. The further evaluation with 39 participants assessed the ability of humans to detect false positives and false negatives manually, without any tool support. The correct classification rate achieved by humans in this case is poor (29%) indicating how helpful our automated approach can be for developers. The comparison of humans’ ability to improve oracles with and without the tool in a study with 29 other participants also empirically validates the effectiveness of the approach

OASIs: oracle assessment and improvement tool

The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. We introduce OASIs, a search-based tool for Java that assists testers in oracle assessment and improvement. It does so by combining test case generation to reveal false positives and mutation testing to reveal false negatives. In this work, we describe how OASIs works, provide details of its implementation, and explain how it can be used in an iterative oracle improvement process with a human in the loop. Finally, we present a summary of previous empirical evaluation showing that the fault detection rate of the oracles after improvement using OASIs increases, on average, by 48.6%

Testing Machine Learning based Systems: A Systematic Mapping

A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1)~the context of the problem they address, (2)~their features, and (3)~their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. The most active research areas in MLS testing address automated scenario/input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks

Testing machine learning based systems: a systematic mapping

Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. Objective: To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. Method: We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. Results: We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. Conclusions: The most active research areas in MLS testing address automated scenario/ input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks