On Learning Meaningful Assert Statements for Unit Test Cases

Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community...

Full description

Saved in:
Bibliographic Details
Published in:2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) pp. 1398 - 1409
Main Authors: Watson, Cody, Tufano, Michele, Moran, Kevin, Bavota, Gabriele, Poshyvanyk, Denys
Format: Conference Proceeding
Language:English
Published: ACM 01.10.2020
Subjects:
Deep Learning
Neural Machine Translation
Testing
Unit Tests
ISSN:1558-1225
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Software testing is an essential part of the software lifecycle and requires a substantial amount of time and effort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called Atlas (AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e., the main method under test), Atlas can predict a meaningful assert statement to assess the correctness of the focal method. We applied Atlas to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, Atlas is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness of our approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, who can benefit from the recommended assert statements while writing test code.
ISSN:1558-1225
DOI:10.1145/3377811.3380429