Junior Researcher at LUT University
Research Topic: Artificial Intelligence in Software Testing
Research Question(s): How will artificial intelligence change the software testing and quality assurance/engineering work in software development?
Want to participate in my study? I’m interested in interviewing people involved in software testing from companies that both do and do not utilize AI in their software testing. “Involved in” means, for example, making decisions (managers), performing testing activities, working on test automation and infrastructure, or working on AI adoption projects in software testing context in any role. As long as you have opinions, expectations, or observations on AI adoption in software testing you want to share.
Want to know what you are agreeing to? Take a look at the information about the research background, your rights as an interviewee, and data privacy and processing .
To participate in the study, all you need to do is book an interview with me. The time reserved per interview is 1h 20 minutes. The interviews are done and recorded via Teams (if this is not ok for you and want to participate, let me know and we can consider other options). Participation in the interviews is voluntary, and you have the right to withdraw from the study at any point (before, during or after the interviews).
When you have booked the interview, you will receive an automatic notification about the booking that contains the link to the Teams meeting. I will also contact you and provide more information about the interview process.
Latest Research
Preprint: Expectations vs Reality – A Secondary Study on AI Adoption in Software Testing
ABSTRACT: “In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities. In some activities, such as coding, AI has already been an everyday tool, but in software testing activities AI it has not yet made a significant breakthrough. In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing, as well as how AI has been adopted in software testing practice. To achieve this, we performed a systematic mapping study of recent (2020 and later) studies on AI adoption in software testing in the industry, and applied thematic analysis to identify common themes and categories, such as the real-world use cases and benefits, in the found papers. The observations suggest that AI is not yet heavily utilized in software testing, and still relatively few studies on AI adoption in software testing have been conducted in the industry context to solve real-world problems. Earlier studies indicated there was a noticeable gap between the actual use cases and actual benefits versus the expectations, which we analyzed further. While there were numerous potential use cases for AI in software testing, such as test case generation, code analysis, and intelligent test automation, the reported actual implementations and observed benefits were limited. In addition, the systematic mapping study revealed a potential problem with false positive search results in online databases when using the search string “artificial intelligence”.”
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