Evolution of Software Testing Strategies and Trends: Semantic Content Analysis of Software Research Corpus of the Last 40 Years

N. Cagiltay A. Soylu Fatih Gurcan + 1 lainnya

2022

The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today’s research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.

S. Matalonga Stefano Faralli J. Hauck + 2 lainnya

17 Agustus 2023

Context: Artificial intelligence (AI) methods and models have extensively been applied to support different phases of the software development lifecycle, including software testing (ST). Several secondary studies investigated the interplay between AI and ST but restricted the scope of the research to specific domains or sub-domains within either area. Objective: This research aims to explore the overall contribution of AI to ST, while identifying the most popular applications and potential paths for future research directions. Method: We executed a tertiary study following well-established guidelines for conducting systematic literature mappings in software engineering and for answering nine research questions. Results: We identified and analyzed 20 relevant secondary studies. The analysis was performed by drawing from well-recognized AI and ST taxonomies and mapping the selected studies according to them. The resulting mapping and discussions provide extensive and detailed information on the interplay between AI and ST. Conclusion: The application of AI to support ST is a well-consolidated and growing interest research topic. The mapping resulting from our study can be used by researchers to identify opportunities for future research, and by practitioners looking for evidence-based information on which AI-supported technology to possibly adopt in their testing processes.

Fauziah Baharom Haslina Mohd Samera Obaid Barraood

2021

Software testing is anessential process for ensuring thequality and reliability of software products. The efficiency of testing activities depends largely on the test case quality, which is considered as one of the major concerns of software testing. Unfortunately, at the moment there is no clear guideline that can be referred by software testers in producing good quality test cases. Hence, producing guideline is certainly required. To construct a pragmatic guideline, it is crucial to identify the factors that lead todesigninggood quality test cases. The existing test case quality factors are not comprehensive and need further investigation and improvement. Therefore,a content analysis was conducted to identify the test case qualityfactors from software testing experts point of view available in the software testing websites. The software testing websites provide explicit information about the quality of test cases in order to avoid the poor design of test cases. Thus, this study presents the outcomes of content analysis from 22 software testing websites which comprise of static content websites and blogs. Consequently, eight (8)factors and their corresponding 30 sub-factors were identified. Among the factors are documentation, manageability, maintainability, reusability, requirement quality, efficiency, tester knowledge, and effectiveness of test cases. These factors are useful to be referred by the practitioners in assuring the quality of the design test cases which implicitly can ensure the quality of the software products. Webology, Volume 18, Special Issue on Artificial Intelligence in Cloud Computing January, 2021 76 http://www.webology.org

Alexandru Marginean N. Alshahwan M. Harman

1 April 2023

There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. In this paper we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.

Farhan Khan Sabrina Alam Mahmudul Islam + 1 lainnya

31 Oktober 2023

Software testing is a crucial component of software development. With the increasing complexity of software systems, traditional manual testing methods are becoming less feasible. Artificial Intelligence (AI) has emerged as a promising approach to software testing in recent years. This review paper aims to provide an in-depth understanding of the current state of software testing using AI. The review will examine the various approaches, techniques, and tools used in this area and assess their effectiveness. The selected articles for this study have been extracted from different research databases using the advanced search string strategy. Initially, 40 articles have been extracted from different research libraries. After gradual filtering finally, 20 articles have been selected for the study. After studying all the selected papers, we find that various testing tasks can be automated successfully using AI (Machine Learning and Deep Learning) such as Test Case Generation, Defect Prediction, Test Case Prioritization Metamorphic Testing, Android Testing, Test Case Validation, and White Box Testing. This study also finds that the integration of AI in software testing is making software testing activities easier along with better performance. This literature review paper provides a thorough analysis of the impact AI can have on the software testing process.