A Generalized approach to the operationalization of Software Quality Models

Clemente Izurieta; Derek Reimanis; Eric O'Donoghue; Kaveen Liyanage; A Redempta Manzi Muneza; Bradley Whitaker; Ann Marie Reinhold

doi:10.7717/peerj-cs.2357

A Generalized approach to the operationalization of Software Quality Models

PeerJ Comput Sci. 2024 Oct 8:10:e2357. doi: 10.7717/peerj-cs.2357. eCollection 2024.

Authors

Clemente Izurieta^{1

2

3}, Derek Reimanis³, Eric O'Donoghue³, Kaveen Liyanage⁴, A Redempta Manzi Muneza³, Bradley Whitaker⁴, Ann Marie Reinhold^{2

3}

Affiliations

¹ Idaho National Laboratory, Idaho Falls, United States of America.
² Pacific Northwest National Laboratory, Richland, United States of America.
³ Gianforte School of Computing, Montana State University, Bozeman, United States of America.
⁴ Electrical and Computer Engineering Department, Montana State University, Bozeman, MT, United States of America.

Abstract

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Keywords: Data aggregation; Data science; Quality assurance; Quality models; Software engineering; Software quality.

Grants and funding

This research was conducted with support from the U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S&T) under contract 70RSAT22CB0000005. Any opinions contained herein are those of the author and do not necessarily reflect those of DHS S&T. This research is also supported by TechLink (TechLink PIA FA8650-23-3-9553). There was no additional external funding received for this study. The Department of Homeland Security Science and Technology (DHS S&T) division reviews our article to ensure no sensitive information is included, and to ensure that the scientific content is sound. This article contains unclassified work only, including all data sources and GitHub repos.