A Catalog of Transformations to Remove Smells From Natural Language Tests

TL;DR

This paper presents a catalog of transformations and a tool to automatically identify and remove seven natural language test smells, improving test quality and reliability in software development.

Contribution

It introduces a systematic catalog and NLP-based tool for removing natural language test smells, filling a gap in existing research and tools.

Findings

Professionals find the transformations valuable

The tool achieves an F-Measure of 83.70% in identifying smells

Empirical evaluation confirms improved test quality

Abstract

Test smells can pose difficulties during testing activities, such as poor maintainability, non-deterministic behavior, and incomplete verification. Existing research has extensively addressed test smells in automated software tests but little attention has been given to smells in natural language tests. While some research has identified and catalogued such smells, there is a lack of systematic approaches for their removal. Consequently, there is also a lack of tools to automatically identify and remove natural language test smells. This paper introduces a catalog of transformations designed to remove seven natural language test smells and a companion tool implemented using Natural Language Processing (NLP) techniques. Our work aims to enhance the quality and reliability of natural language tests during software development. The research employs a two-fold empirical strategy to evaluate its contributions. First, a survey involving 15 software testing professionals assesses the acceptance and usefulness of the catalog's transformations. Second, an empirical study evaluates our tool to remove natural language test smells by analyzing a sample of real-practice tests from the Ubuntu OS. The results indicate that software testing professionals find the transformations valuable. Additionally, the automated tool demonstrates a good level of precision, as evidenced by a F-Measure rate of 83.70%