Impact of Request Formats on Effort Estimation: Are LLMs Different Than Humans?

Software development Effort Estimation (SEE) comprises predicting the most realistic amount of effort (e.g., in work hours) required to develop or maintain software based on incomplete, uncertain, and noisy input. Expert judgment is the dominant SEE strategy used in the industry. Yet, expert-based judgment can provide inaccurate effort estimates, leading to projects’ poor budget planning and cost and time overruns, negatively impacting the world economy. Large Language Models (LLMs) are good candidates to assist software professionals in effort estimation. However, their effective leveraging for SEE requires thoroughly investigating their limitations and to what extent they overlap with those of (human) software practitioners. One primary limitation of LLMs is the sensitivity of their responses to prompt changes. Similarly, empirical studies showed that changes in the request format (e.g., rephrasing) could impact (human) software professionals’ effort estimates. This paper reports the first study that replicates a series of SEE experiments, which were initially carried out with software professionals (humans) in the literature. Our study aims to investigate how LLMs’ effort estimates change due to the transition from the traditional request format (i.e., "How much effort is required to complete X?”) to the alternative request format (i.e., "How much can be completed in Y work hours?”). Our experiments involved three different LLMs (GPT-4, Gemini 1.5 Pro, Llama 3.1) and 88 software project specifications (per treatment in each experiment), resulting in 880 prompts, in total that we prepared using 704 user stories from three large-scale open-source software projects (Hyperledger Fabric, Mulesoft Mule, Spring XD). Our findings align with the original experiments conducted with software professionals: The first four experiments showed that LLMs provide lower effort estimates due to transitioning from the traditional to the alternative request format. The findings of the fifth and first experiments detected that LLMs display patterns analogous to anchoring bias, a human cognitive bias defined as the tendency to stick to the anchor (i.e., the "Y work-hours” in the alternative request format). Our findings provide crucial insights into facilitating future human-AI collaboration and prompt designs for improved effort estimation accuracy.