An empirical study to compare three web test automation approaches: NLP-based, programmable, and capture&replay

A new advancement in test automation is the use of natural language processing (NLP) to generate test cases (or test scripts) from natural language text. NLP is innovative in this context and promises of reducing test cases creation time and simplifying understanding for "non-developer" software testers as well. Recently, many vendors have launched on the market many proposals of NLP-based tools and testing frameworks but their superiority has never been empirically validated. This paper investigates the adoption of NLP-based test automation in the web context with a series of case studies conducted to compare the costs of the NLP testing approach-measured in terms of test cases development and test cases evolution-with respect to more consolidated approaches, that is, programmable (or script-based) testing and capture & replay testing. The results of our study show that NLP-based test automation appears to be competitive for small- to medium-sized test suites such as those considered in our empirical study. It minimizes the total cumulative cost (development and evolution) and does not require software testers with programming skills.