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Towards an Open-Source Tool for Measuring and Visualizing the Interest of Technical Debt

October 2015 Presentation
Davide Falessi (California Polytechnic State University), Andreas Reichel (Mannheim University of Applied Sciences)

This work advances the measurement and visualization of interest on technical debt and introduces MIND, an open-source tool that supports quantification of interest.

Publisher:

Software Engineering Institute

This presentation was created for a conference series or symposium and does not necessarily reflect the positions and views of the Software Engineering Institute.

Abstract

This presentation was part of the Seventh International Workshop on Managing Technical Debt, held in conjunction with the 31th International Conference on Software Maintenance and Evolution (ICSME 2015).

Current tools for managing technical debt are able to report the principal of the debt, i.e., the amount of effort required to fix all the quality rules violated in a project. However, they do not report the interest, i.e., the disadvantages the project had or will have due to quality rules violations. As a consequence, the user lacks support in understanding how much the principal should be reduced and why. We claim that information about the interest is, at least, as important as the information about the principal; the interest should be quantified and treated as a first-class entity like the principal. In this paper we aim to advance the state of the art of how the interest is measured and visualized. The goal of the paper is to describe MIND, an open-source tool which is, to the best of our knowledge, the first tool supporting the quantification and visualization of the interest. MIND, by analyzing historical data coming from Redmine and Git repositories, reports the interest incurring in a software project in terms of how many extra defects occurred, or will occur, due to quality rules violations. We evaluated MIND by using it to analyze a software project stored in a dataset of more than a million lines of code. Results suggest that MIND accurately measures the interest of technical debt.