A Vision for Debiasing Confirmation Bias in Software Testing via LLM

Background: Large language models (LLM) suffer from various forms of biases due to the biased datasets used to train the models. At the same time, human cognitive biases have an equal propensity to express themselves when using LLMs for software engineering tasks. Software testing is a critical phase of the software development life cycle. Confirmation bias is reported to have deteriorated software testing by designing more specification-consistent test cases compared to specificationinconsistent test cases. However, there is a lack of debiasing (mitigation) strategies in this regard. Aims: In this paper, first, we investigate whether the LLM model suffers from confirmation bias while performing software testing tasks. Second, we propose a vision of debasing confirmation bias in software testing via LLM. Method: We conducted an empirical study to detect confirmation bias by an LLM (ChatGPT4.0) in the design of functional test cases. Based on empirical findings, we used the analytical paradigm to design a multi-agent system. Results: We present a vision for debiasing confirmation bias in functional software testing by leveraging LLMs via a multi-agent approach. Conclusions: The proposed vision may improve the performance of LLMs in terms of reduced confirmation bias and serve as a debiasing technique for functional software testing.