Related papers: Improving Test Automation Maturity: a Multivocal L…
Test automation is important in software industry but self-assessment instruments for assessing its maturity are not sufficient. The two objectives of this study are to synthesize what an organization should focus to assess its test…
The software industry has seen an increasing interest in test automation. In this paper, we present a test automation maturity survey serving as a self-assessment for practitioners. Based on responses of 151 practitioners coming from above…
Context: Maturity of practices and infrastructure in healthcare domain directly impacts the quality and efficiency of healthcare services. Therefore, various healthcare administrations (e.g., hospital management to nation-wide health…
Systematic literature reviews (SLR) have been increasingly conducted in software engineering and they provide significant benefits in terms of summarizing the state of the research. The process of conducting SLR is complex, involving…
Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML…
Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both…
Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for severe financial and human losses. Software testing is however…
Context: Software testing plays an essential role in product quality improvement. For this reason, several software testing models have been developed to support organizations. However, adoption of testing process models inside…
Embedded systems are ubiquitous and play critical roles in management systems for industry and transport. Software failures in these domains may lead to loss of production or even loss of life, so the software in these systems needs to be…
Context: Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations such as high test suite maintenance costs and the need for extensive programming skills. Artificial Intelligence (AI)…
Given the large number of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands of…
Context: The rise of Artificial Intelligence (AI) in software engineering has led to the development of AI-powered test automation tools, promising improved efficiency, reduced maintenance effort, and enhanced defect-detection. However, a…
The popularity of continuous integration (CI) is increasing as a result of market pressure to release product features or updates frequently. The ability of CI to deliver quality at speed depends on reliable test automation. In this paper,…
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating contexts, that degrade model performance are a common occurrence…
The software development lifecycle depends heavily on the testing process, which is an essential part of finding issues and reviewing the quality of software. Software testing can be done in two ways: manually and automatically. With an…
Usability issues can hinder the effective use of software. Therefore, various techniques are deployed to diagnose and mitigate them. However, these techniques are costly and time-consuming, particularly in iterative design and development.…
This study aims to information security in academic information systems to provide recommendations for improvements in information security management by the expected maturity level based on ISO/IEC 27002:2013. By using a qualitative…
Maturity models for software indicate the key areas that contribute to quality improvements. They usually combine technical, organisational and human aspects relevant for effective software development, to focus the efforts and draw the…
Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and…
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 amount of approaches in this…