Not All Backports Are Equal: Analyzing the Risk/Value Trade-offs in Backport Acceptance Decisions

Ensuring the long-term stability and security of widely-used software is a critical operational challenge. A key part of this is “backporting,” the high-stakes process of deciding which updates to apply to stable product versions. Currently, developers often make these crucial risk-versus-value judgments without formal, data-driven guidance. This project will develop an intelligent decision-support framework to solve this problem. By applying state-of-the-art machine learning to historical project data, our system will learn to automatically classify backport proposals, profile their risk and value, and provide actionable recommendations. For the participating institutions, this project pioneers new techniques in AI-driven software engineering, culminating in a tangible decision-support tool. The ultimate benefit is a direct contribution to enhancing software reliability and developer productivity, strengthening the innovation capacity of the wider technology sector.

Faculty Supervisor:

Moataz Chouchen

Student:

Partner:

National School of Computer Science (ENSI), Tunisia

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

Concordia University

Program:

Globalink Research Award

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