Registered user since Thu 9 Jul 2020
Contributions
Research Papers
Thu 13 Oct 2022 17:30 - 17:50 at Room 128 - Technical Session 30 - Builds and Dependencies Chair(s): Christian KästnerTraceability establishes trace links among software artifacts (e.g., requirements and code) based on whether two artifacts relate to the same part of system functionalities. These trace links are valuable for software development process, but are difficult to obtain manually. To cope with the costly and fallible manual recovery, researchers proposed many automated approaches that help to recover trace links through the textual similarities among software artifacts, such as approaches based on Information Retrieval (IR). However, the low quality and the low quantity of artifact texts negatively impact the calculated textual similarities, thus greatly hindering the performance of IR-based approaches. In this study, we propose to extract co-occurred word pairs from the text structures of both requirements and code (i.e., consensual biterms) to improve IR-based traceability recovery. Specifically, we first collect a set of biterms based on the part-of-speech of requirement texts, and then filter them through the code texts. We then use these consensual biterms to both enrich the input corpus for IR techniques and enhance the calculations of IR values. An empirical evaluation based on nine real-world systems shows that our approach can not only outperform baseline approaches, but also achieve a significant complementary effect with other enhancing strategies from different perspectives.
Pre-printTool Demonstrations
Tue 11 Oct 2022 11:10 - 11:20 at Banquet B - Technical Session 3 - Fuzzing I Chair(s): Aravind MachiryWith the rapid development of autonomous driving systems (ADS), especially the increasing adoption of deep neural networks (DNNs) as their core components, effective quality assurance methods for ADS have attracted growing interests in both academia and industry. In this paper, we report a new testing platform ADEPT we have developed, aiming to provide practically realistic and comprehensive testing facilities for DNN-based ADS. ADEPT is based on the virtual simulator CARLA and provides numerous testing facilities such as scene construction, ADS importation, test execution and recording, etc. In particular, ADEPT features two distinguished test scenario generation strategies designed for autonomous driving. First, we make use of real-life accident reports from which we leverage natural language processing to fabricate abundant driving scenarios. Second, we synthesize physically-robust adversarial attacks by taking the feedback of ADS into consideration and thus are able to generate closed-loop test scenarios. The experiments confirm the efficacy of the platform. A video demonstrating the usage of ADEPT can be found at https://youtu.be/evMorf0uR_s.
