Towards an Interpretable Analysis for Estimating the Resolution Time of Software Issues

TL;DR

This paper presents an interpretable system that uses topic modeling and metadata to accurately estimate software issue resolution times, addressing limitations of previous methods that overlooked semantics and actual effort.

Contribution

It introduces a novel approach combining topic modeling and metadata analysis for more accurate and interpretable bug-fix time prediction across projects.

Findings

Effective resolution time estimation across multiple projects

Incorporates semantics and metadata for interpretability

Outperforms existing methods in accuracy

Abstract

Lately, software development has become a predominantly online process, as more teams host and monitor their projects remotely. Sophisticated approaches employ issue tracking systems like Jira, predicting the time required to resolve issues and effectively assigning and prioritizing project tasks. Several methods have been developed to address this challenge, widely known as bug-fix time prediction, yet they exhibit significant limitations. Most consider only textual issue data and/or use techniques that overlook the semantics and metadata of issues (e.g., priority or assignee expertise). Many also fail to distinguish actual development effort from administrative delays, including assignment and review phases, leading to estimates that do not reflect the true effort needed. In this work, we build an issue monitoring system that extracts the actual effort required to fix issues on a per-project basis. Our approach employs topic modeling to capture issue semantics and leverages metadata (components, labels, priority, issue type, assignees) for interpretable resolution time analysis. Final predictions are generated by an aggregated model, enabling contributors to make informed decisions. Evaluation across multiple projects shows the system can effectively estimate resolution time and provide valuable insights.