A Learning Algorithm for Change Impact Prediction

TL;DR

This paper introduces LCIP, a learning algorithm that predicts the impact of code changes on test failures in object-oriented software, demonstrating promising accuracy on Java applications.

Contribution

The paper presents a novel learning-based approach, LCIP, for change impact prediction that leverages past impact data to improve future impact forecasts.

Findings

LCIP achieves 69% precision in impact prediction.

Recall of 79% indicates high detection rate.

F-Score of 55% balances precision and recall.

Abstract

Change impact analysis consists in predicting the impact of a code change in a software application. In this paper, we take a learning perspective on change impact analysis and consider the problem formulated as follows. The artifacts that are considered are methods of object-oriented software, the change under study is a change in the code of the method, the impact is the test methods that fail because of the change that has been performed. We propose an algorithm, called LCIP that learns from past impacts to predict future impacts. To evaluate our system, we consider 7 Java software applications totaling 214,000+ lines of code. We simulate 17574 changes and their actual impact through code mutations, as done in mutation testing. We find that LCIP can predict the impact with a precision of 69%, a recall of 79%, corresponding to a F-Score of 55%.