1,721,011 research outputs found
A model-driven engineering approach for supporting questionnaire-based gap analysis processes through application lifecycle management systems
No full textGap analysis is a common approach in industry to evaluate the gaps between the implemented software processes and the requirements of process quality frameworks or standard norms. Gap analysis processes are usually executed by means of questionnaires that need to be crafted ad hoc according to specific appraisal goals. The approaches used for developing, compiling and evaluating the answers given to these questionnaires usually do not follow well-defined methodologies or processes, and lack adequate tool support. This paper aims at investigating novel approaches for the execution of questionnaire-based gap analysis (QBGA) processes in industrial practices. We propose the adoption of state-of-the-art software engineering technologies and methodologies like application lifecycle management (ALM) and model-driven engineering (MDE) to support these processes. We perform an industrial survey for understanding the main issues affecting questionnaire-based gap analysis processes in industrial practices. We exploit model-driven engineering for building an ALM-based tool that supports the QBGA process execution and allows us to overcome the emerged process issues. We implement the GADGET tool to apply the MDE approach we use for developing the ALM-based tool. The feasibility of the proposed approach has been evaluated by a case study conducted in the automotive industrial domain. Two different QBGA processes have been configured and implemented in an ALM system with the support of the GADGET tool. The resulting ALM tool was used to perform the gap analysis processes. Semi-structured interviews with the involved industrial personnel were conducted to carry out a qualitative evaluation. The case study results show that the introduction of ALM improves the quality of the questionnaire-based gap analysis processes. Moreover, the adoption of model-driven engineering approach implemented by the GADGET tool provides a viable solution for configuring application lifecycle management systems and supporting the process execution
AI in GUI-Based Software Testing: Insights from a Survey with Industrial Practitioners
No full textIn today’s technology-driven world, there is a growing interest in leveraging Artificial Intelligence (AI) to streamline software testing processes. Our research delves into GUI-based testing, a prominent technique for verifying software functionality. Preliminary findings from our industrial survey of 45 respondents provide insights into the use of AI in GUI-based software testing. The survey aims to understand how AI supports GUI-based testing, the AI techniques and tools used, and the perceived advantages and limitations.
The collected results suggest a diffuse yet superficial utilization of AI-based mechanisms among GUI-based testers. Practitioners often employ AI techniques in a technology-agnostic way, treating commercial tools as black boxes. These findings underscore the need for additional research aimed at gaining a deeper understanding of the AI techniques and tools employed in industry and their intended purposes
EXACT: A tool for comprehending VBA-based Excel spreadsheet applications
No full textSpreadsheet applications are widely adopted by millions of end users from several application domains and provide strategic support to many business, scientific, industrial, and organizational processes. These applications are usually developed by rapid application development processes, exploiting host scripting languages allowing the basic spreadsheets to provide complex functionality, business rules, and user interfaces. Several factors complicate the comprehension of these applications because they are usually developed and maintained by end users without specific software engineering skills, grow over time, are not adequately documented, and do not present explicit separation between data, business logic, and user interface layers. This paper presents a reverse engineering tool intended to support the comprehension of Excel spreadsheet applications developed using the Visual Basic for Application programming language. The tool has been implemented as an add-in that extends the Excel working environment by providing analysis and visualization features. It is able to extract information about the elements composing the analyzed Excel spreadsheet application, the functionality it exposes through its user interface, and the dependencies among its cells. This information is provided by means of interactive views. The validity of the tool has been assessed by a qualitative case study performed with professional end users from an automotive industrial domain
Automated functional testing of mobile applications: a systematic mapping study
No full textContext Testing is a critical and costly activity in the life cycle of a mobile application, due to the growing request of new applications and to the rapid evolution of mobile devices and frameworks. Testing automation may represent an effective solution to improve the quality of mobile applications and to reduce testing costs. Objective We have performed a systematic mapping study to find, analyze, and classify papers in the scientific literature that are related to the automation of functional testing of mobile applications with the aim to provide a classification scheme useful for researchers and practitioners to have a clear view of the state of the art and to easily find existing solutions to their issues. Method We have conducted the study on the basis of a set of 18 research questions. Search queries have been formulated and applied to 7 search engines and the resulting papers have been filtered by considering sets of inclusion and exclusion criteria. The selected papers have been systematically classified and, in addition, a bibliometric analysis has been performed. Results A systematic map including 131 papers has been obtained and is publicly available. The papers have been classified on the basis of the supported testing activities, the characteristics of the techniques and tools they present, and the evaluation methodologies adopted to validate them. The bibliometric analysis has allowed the identification of the most active researchers, the most attractive venues, and the most influential papers. Conclusions The analysis of the systematic mapping has allowed the identification of some research trends and gaps in this field of study. For example, we have observed a strong prevalence of Android-based approaches, a lack of contributions from industry, and the absence of specific venues and journals focused on mobile testing automation
AI in GUI-based testing: A survey of techniques, tools, and perceived advantages and limitations
No full textBackground: The adoption of Artificial Intelligence (AI) techniques in Software Testing (ST) has grown rapidly, particularly in response to the increasing complexity of modern systems. In GUI-based testing, AI is often cited as a promising means to automate repetitive tasks and improve testing efficiency. However, the actual use of AI in this domain remains underexplored through systematic empirical investigation.
Objective: This study aims to analyze how AI is adopted in GUI-based testing, identifying the techniques and tools employed, the testing activities they support, and the perceived benefits and limitations.
Method: We conducted a large-scale survey involving 107 participants from both academia and industry. The survey focuses on three core testing activities: test case definition, test oracle design, and test case optimization. It extends a prior study based on interviews with 45 industry practitioners.
Results:Findings show that AI is primarily used to support test case definition, with techniques such as Natural Language Processing, Optimization, and Large Language Models (LLMs) being the most common. AI also provides support in test oracle design, where image processing and knowledge representation play key roles, and in test suite optimization, through the use of supervised learning, reinforcement learning, and search-based techniques.
Conclusion: The paper identifies ongoing challenges and outlines future directions, including the need for transparent AI tools, guidelines for LLM integration, and the deployment of a continuously open survey to monitor trends in AI adoption over time
A GUI Crawling-based technique for Android Mobile Application Testing
Full text linkAs mobile applications become more complex, specific development tools and frameworks as well as cost-effective testing techniques and tools will be essential to assure the development of secure, high-quality mobile applications. This paper addresses the problem of automatic testing of mobile applications developed for the Google Android platform, and presents a technique for rapid crash testing and regression testing of Android applications. The technique is based on a crawler that automatically builds a model of the application GUI and obtains test cases that can be automatically executed. The technique is supported by a tool for both crawling the application and generating the test cases. In the paper we present an example of using the technique and the tool for testing a real small size Android application that preliminary shows the effectiveness and usability of the proposed testing approach
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