Defect patterns and software metric correlations in a mature ubiquitous system

TL;DR

This study analyzes 30 years of defect data in a mature Fortran numerical library to challenge existing beliefs about defect patterns and correlations, providing empirical insights into long-term software behavior.

Contribution

It offers the first long-term empirical analysis of defect growth, clustering, and metric correlations in a well-documented, mature software system, questioning established generalizations.

Findings

Defect clustering is empirically supported in long-term data.

Long-term defect patterns challenge language-independent beliefs.

Empirical data can recalibrate generalizations in software engineering.

Abstract

Software engineering is not an empirically based discipline. Consequently, many of its practices are based on little more than a generally agreed feeling that something may be true. Part of the problem is that it is both relatively young and unusually rich in new and often competing methodologies. As a result, there is little time to infer important empirical patterns of behaviour before the technology moves on. Very occasionally an opportunity arises to study the defect growth and patterns in a well-specified software system which is also well-documented and heavily-used over a very long period. Here we analyse the defect growth and structural patterns in just such a system, a numerical library written in Fortran evolving over a period of 30 years. This is important to the wider community for two reasons. First, the results cast significant doubt on widely-held long standing language-independent beliefs and second, some of these beliefs are perpetuated in modern technologies. It therefore makes good sense to use empirical long-term data as it becomes available to re-calibrate those generalisations. Finally, we analyse the phenomenon of defect clustering providing further empirical support for its existence.