References of "Panichella, Annibale"
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See detailLog-based Slicing for System-level Test Cases
Messaoudi, Salma UL; Shin, Donghwan UL; Panichella, Annibale et al

in Proceedings of ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (2021, July)

Regression testing is arguably one of the most important activities in software testing. However, its cost-effectiveness and usefulness can be largely impaired by complex system test cases that are poorly ... [more ▼]

Regression testing is arguably one of the most important activities in software testing. However, its cost-effectiveness and usefulness can be largely impaired by complex system test cases that are poorly designed (e.g., test cases containing multiple test scenarios combined into a single test case) and that require a large amount of time and resources to run. One way to mitigate this issue is decomposing such system test cases into smaller, separate test cases---each of them with only one test scenario and with its corresponding assertions---so that the execution time of the decomposed test cases is lower than the original test cases, while the test effectiveness of the original test cases is preserved. This decomposition can be achieved with program slicing techniques, since test cases are software programs too. However, existing static and dynamic slicing techniques exhibit limitations when (1) the test cases use external resources, (2) code instrumentation is not a viable option, and (3) test execution is expensive. In this paper, we propose a novel approach, called DS3 (Decomposing System teSt caSe), which automatically decomposes a complex system test case into separate test case slices. The idea is to use test case execution logs, obtained from past regression testing sessions, to identify "hidden" dependencies in the slices generated by static slicing. Since logs include run-time information about the system under test, we can use them to extract access and usage of global resources and refine the slices generated by static slicing. We evaluated DS3 in terms of slicing effectiveness and compared it with a vanilla static slicing tool. We also compared the slices obtained by DS3 with the corresponding original system test cases, in terms of test efficiency and effectiveness. The evaluation results on one proprietary system and one open-source system show that DS3 is able to accurately identify the dependencies related to the usage of global resources, which vanilla static slicing misses. Moreover, the generated test case slices are, on average, 3.56 times faster than original system test cases and they exhibit no significant loss in terms of fault detection effectiveness. [less ▲]

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See detailAutomated Repair of Feature Interaction Failures in Automated Driving Systems
Ben Abdessalem, Raja; Panichella, Annibale; Nejati, Shiva UL et al

in Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020) (2020, July)

In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated ... [more ▼]

In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated repair techniques can resolve failures that arise at the system-level and are caused by undesired interactions among different system components or functions. Feature interaction failures are common in complex systems such as autonomous cars that are typically built as a composition of independent features (i.e., units of functionality). In this paper, we propose a repair technique to automatically resolve undesired feature interaction failures in automated driving systems (ADS) that lead to the violation of system safety requirements. Our repair strategy achieves its goal by (1) localizing faults spanning several lines of code, (2) simultaneously resolving multiple interaction failures caused by independent faults, (3) scaling repair strategies from the unit-level to the system-level, and (4) resolving failures based on their order of severity. We have evaluated our approach using two industrial ADS containing four features. Our results show that our repair strategy resolves the undesired interaction failures in these two systems in less than 16h and outperforms existing automated repair techniques. [less ▲]

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See detailTesting Autonomous Cars for Feature Interaction Failures using Many-Objective Search
Ben Abdessalem (helali), Raja UL; Panichella, Annibale; Nejati, Shiva UL et al

in Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018) (2018)

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ... [more ▼]

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting our approach into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements. [less ▲]

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