References of "ACM Transactions on Software Engineering and Methodology"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailAn Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction
Arora, Chetan UL; Sabetzadeh, Mehrdad UL; Nejati, Shiva UL et al

in ACM Transactions on Software Engineering and Methodology (2019), 28(1),

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to ... [more ▼]

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous, i.e., not relevant to the domain model. Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements, and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance. [less ▲]

Detailed reference viewed: 412 (89 UL)
Full Text
Peer Reviewed
See detailOracles for Testing Software Timeliness with Uncertainty
Wang, Chunhui UL; Pastore, Fabrizio UL; Briand, Lionel UL

in ACM Transactions on Software Engineering and Methodology (2019), 28(1),

Uncertainty in timing properties (e.g., detection time of external events) is a common occurrence in embedded software systems since these systems interact with complex physical environments. Such time ... [more ▼]

Uncertainty in timing properties (e.g., detection time of external events) is a common occurrence in embedded software systems since these systems interact with complex physical environments. Such time uncertainty leads to non-determinism. For example, time-triggered operations may either generate different valid outputs across different executions, or experience failures (e.g., results not being generated in the expected time window) that occur only occasionally over many executions. For these reasons, time uncertainty makes the generation of effective test oracles for timing requirements a challenging task. To address the above challenge, we propose STUIOS (Stochastic Testing with Unique Input Output Sequences), an approach for the automated generation of stochastic oracles that verify the capability of a software system to fulfill timing constraints in the presence of time uncertainty. Such stochastic oracles entail the statistical analysis of repeated test case executions based on test output probabilities predicted by means of statistical model checking. Results from two industrial case studies in the automotive domain demonstrate that this approach improves the fault detection effectiveness of tests suites derived from timed automata, compared to traditional approaches. [less ▲]

Detailed reference viewed: 175 (47 UL)