Importance of manual testing : empirical analysis of contexts where manual testing brings value
Tishkovskaya, Elena (2026)
Diplomityö
2026
School of Engineering Science, Tietotekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2026060564401
https://urn.fi/URN:NBN:fi-fe2026060564401
Tiivistelmä
Recently, the demand for test automation has grown significantly, and manual testing may appear neglected in companies’ testing strategies. In addition, the improving quality of AI tools has turned them into everyday collaborators in software development and testing activities.
This thesis investigates how manual testing contributes to product and service quality alongside automated testing. It also examines how human judgement, cognition, curiosity, and domain expertise support software testing and defect identification. Finally, the study explores how AI can assist testers in manual activities and what limitations such assistance currently has.
The thesis reviews the theory behind manual and automated testing and the contexts in which these approaches are beneficial, supported by previous research and industry practices. The empirical research was conducted as a case study using both qualitative and quantitative methods. It focused on interviews with testing specialists from seven Finnish B2B companies and was complemented by a survey targeted at software testing practitioners.
The findings show that manual testing is necessary in every studied company and helps identify defects that automated tests cannot detect for various reasons. Domain expertise was reported as one of the most important human contributions to software testing. AI was widely used as an assistant, but its limitations mean that human supervision and judgement are still required.
This thesis investigates how manual testing contributes to product and service quality alongside automated testing. It also examines how human judgement, cognition, curiosity, and domain expertise support software testing and defect identification. Finally, the study explores how AI can assist testers in manual activities and what limitations such assistance currently has.
The thesis reviews the theory behind manual and automated testing and the contexts in which these approaches are beneficial, supported by previous research and industry practices. The empirical research was conducted as a case study using both qualitative and quantitative methods. It focused on interviews with testing specialists from seven Finnish B2B companies and was complemented by a survey targeted at software testing practitioners.
The findings show that manual testing is necessary in every studied company and helps identify defects that automated tests cannot detect for various reasons. Domain expertise was reported as one of the most important human contributions to software testing. AI was widely used as an assistant, but its limitations mean that human supervision and judgement are still required.