Manual Tests Do Smell! Cataloging and Identifying Natural Language Test Smells

TL;DR

This paper introduces a catalog of eight natural language test smells in manual tests, validated through empirical analysis and NLP-based detection, improving test maintainability and reliability.

Contribution

It presents the first comprehensive catalog of test smells for manual, natural language tests, along with detection rules and an NLP tool validated in real-world systems.

Findings

Eight test smells identified in manual tests

NLP tool achieved 93.5% F-measure in detection

80.7% of test professionals agreed with the catalog

Abstract

Background: Test smells indicate potential problems in the design and implementation of automated software tests that may negatively impact test code maintainability, coverage, and reliability. When poorly described, manual tests written in natural language may suffer from related problems, which enable their analysis from the point of view of test smells. Despite the possible prejudice to manually tested software products, little is known about test smells in manual tests, which results in many open questions regarding their types, frequency, and harm to tests written in natural language. Aims: Therefore, this study aims to contribute to a catalog of test smells for manual tests. Method: We perform a two-fold empirical strategy. First, an exploratory study in manual tests of three systems: the Ubuntu Operational System, the Brazilian Electronic Voting Machine, and the User Interface of a large smartphone manufacturer. We use our findings to propose a catalog of eight test smells and identification rules based on syntactical and morphological text analysis, validating our catalog with 24 in-company test engineers. Second, using our proposals, we create a tool based on Natural Language Processing (NLP) to analyze the subject systems' tests, validating the results. Results: We observed the occurrence of eight test smells. A survey of 24 in-company test professionals showed that 80.7% agreed with our catalog definitions and examples. Our NLP-based tool achieved a precision of 92%, recall of 95%, and f-measure of 93.5%, and its execution evidenced 13,169 occurrences of our cataloged test smells in the analyzed systems. Conclusion: We contribute with a catalog of natural language test smells and novel detection strategies that better explore the capabilities of current NLP mechanisms with promising results and reduced effort to analyze tests written in different idioms.