Automating test oracles from restricted natural language agile requirements. Issue 1 (22nd July 2020)

• Record Type: Journal Article
• Authors: Malik, Maryam Imtiaz; Sindhu, Muddassar Azam; Khattak, Akmal Saeed; Abbasi, Rabeeh Ayaz; Saleem, Khalid
• Other Names: Gupta Deepak guestEditor.; Rodrigues Joel J. P. C. guestEditor.; Castillo Oscar guestEditor.; Herrero Álvaro guestEditor.; Jiménez Alfredo guestEditor.; Bayraktar Secil guestEditor.; Arroyo Angel guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-22
• Subjects: automated test oracle -- automated test‐driven development -- behaviour‐driven development -- restricted natural language -- agile requirements; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12608
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Manual testing of software requirements written in natural language for agile or any other methodology requires more time and human resources. This leaves the testing process error prone and time consuming. For satisfied end users with bug‐free software delivered on time, there is a need to automate the test oracle process for natural language or informal requirements. The automation of the test oracle is relatively easier with formal requirements, but this task is difficult to achieve with natural language requirements. This study proposes an approach called Restricted Natural Language Agile Requirements Testing (ReNaLART) to automate the test oracle from restricted natural language agile requirements. For this purpose, it uses an existing user story template with some modifications for writing user stories. This helps in identifying test input and expected output for a user story. For comparison of expected and observed outputs it makes use of a regex pattern and string distance functions. It is capable of assigning different types of verdicts automatically depending upon the similarity/dissimilarity between observed and expected outputs of user stories. ReNaLART is validated using several case studies of different domains, namely, OLX Pakistan, Mental Health Tests, McDelivery Pakistan, BlueStacks, Power Searching with Google, TensorFlow Playground, w3Schools 2018 offline and Touch'D. It revealed several faults in five of the above listed eight applications. Plus, the proposed test oracle on an average took 0.02 s for test data generation, expected output generation and verdict assignment. Both these facts show the fault revealing effectiveness and efficiency of ReNaLART.