559 citations · 1.1k across the 34 of their papers we have counts for
38 papers
Has LLM Screening Performance Stalled in Software Engineering Systematic Reviews?
Aleksi Huotala, Miikka Kuutila, Mika Mäntylä
Screening in systematic reviews (SRs) is manual and time-consuming. Prior work has explored large language models (LLMs) for automating this step, but LLMs are evolving rapidly, so…
Fault Injection in OpenAPI Specifications for Evaluating Black-Box Testing Effectiveness
Hamza Bin Mazhar, Yuqing Wang, Mika V. Mäntylä
OpenAPI specifications are the primary input for black-box testing tools in microservice systems (MSS), yet prior work shows these specifications are often incomplete, inconsistent…
Understanding LLMs in Title-Abstract Screening: From Disagreements to Recommendations
Mika Mäntylä, Patricia Matsubara, Katia Romero Felizardo +5
Several studies have examined the use of large language models (LLMs) for title-abstract screening in systematic reviews (SRs), reporting mixed accuracy. However, questions of reli…
Assessing REST API Test Generation Strategies with Log Coverage
Nana Reinikainen, Mika Mäntylä, Yuqing Wang
Assessing the effectiveness of REST API tests in black-box settings can be challenging due to the lack of access to source code coverage metrics and polyglot tech stack. We propose…
Token Interdependency Parsing (Tipping) -- Fast and Accurate Log Parsing
Shayan Hashemi, Mika Mäntylä
In the last decade, an impressive increase in software adaptions has led to a surge in log data production, making manual log analysis impractical and establishing the necessity fo…
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning
Yuqing Wang, Mika V. Mäntylä, Serge Demeyer +3
Microservice-based systems (MSS) may fail with various fault types. While existing AIOps methods excel at detecting abnormal traces and locating the responsible service(s), human e…