Learning Effective Changes for Software Projects

TL;DR

This paper introduces XTREE and BELLTREE algorithms designed to generate actionable plans for software project improvements, addressing decision-making by providing clear guidance within and across projects.

Contribution

It presents novel algorithms that support actionable analytics for software project decision-making, moving beyond mere prediction to practical planning.

Findings

XTREE and BELLTREE effectively generate actionable plans.

Plans lead to measurable improvements in software quality.

The approach supports decision-making across multiple projects.

Abstract

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.