NLP-assisted software testing: a systematic review

Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large number of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques, benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification) and systematic literature review. After compiling an initial pool of 57 papers, we conducted a systematic voting, and our final pool included 50 technical papers. Results: This review paper provides an overview of contribution types in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Among our results are the followings: (1) only 2 of the 28 tools (7%) presented in the papers are available for download; (2) a larger ratio of the papers (23 of 50) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: We believe that this paper would benefit both practitioners and researchers by serving as an "index" to the body of knowledge in this area. The results could help practitioners by enabling them to utilize any of the existing NLP-based techniques to reduce cost of test-case design and decrease the amount of human resources spent on test activities. Initial insights, after sharing this review with some of our industrial collaborators, show that this review can indeed be useful and beneficial to practitioners.