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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Tue 22 Sep 2020 17:10 - 17:30 at Koala - AI for Software Engineering (1) Chair(s): Tingting Yu

Automated test case generation tools have been successfully pro- posed to reduce the amount of human and infrastructure resources required to write and run test cases. However, recent studies demonstrate that the readability of generated tests is very limited due to (i) uninformative identifiers and (ii) lack of proper documentation. Prior studies proposed techniques to improve test readability by either generating natural language summaries or meaningful methods names. While these approaches are shown to improve test readability, they are also affected by two limitations: (1) generated summaries are often perceived as too verbose and redundant by developers, and (2) readable tests require both proper method names but also meaningful identifiers (within-method readability). In this work, we combine template based methods and Deep Learning (DL) approaches to automatically generate test case scenarios (elicited from natural language patterns of test case statements) as well as to train DL models on path-based representations of source code to generate meaningful identifier names. Our ap- proach, called DeepTC-Enhancer , recommends documentation and identifier names with the ultimate goal of enhancing readability of automatically generated test cases. An empirical evaluation with 36 external and internal developers shows that (1) DeepTC-Enhancer outperforms significantly the baseline approach for generating summaries and performs equally with the baseline approach for test case renaming, (2) the transformation proposed by DeepTC-Enhancer result in a significant increase in readability of automatically generated test cases, and (3) there is a significant difference in the feature preferences between external and internal developers.

Tue 22 Sep

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17:10 - 18:10
AI for Software Engineering (1)NIER track / Research Papers at Koala
Chair(s): Tingting Yu University of Kentucky
DeepTC-Enhancer: Improving the Readability of Automatically Generated Tests
Research Papers
Devjeet Roy Washington State University, Ziyi Zhang Washington State University, Maggie Ma Washington State University, Venera Arnaoudova Washington State University, Annibale Panichella Delft University of Technology, Sebastiano Panichella Zurich University of Applied Sciences, Danielle Gonzalez Rochester Institute of Technology, USA, Mehdi Mirakhorli Rochester Institute of Technology
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
Research Papers
Mohammad Jafar Mashhadi University of Calgary, Hadi Hemmati University of Calgary
On Benign Features in Malware Detection
NIER track
Michael Cao The University of British Columbia, Sahar Badihi University of British Columbia, Canada, Khaled Ahmed The University of British Columbia, Peiyu Xiong The University of British Columbia, Julia Rubin University of British Columbia, Canada