The broken windows theory applies to technical debt

• Published in: Empirical software engineering : an international journal Vol. 29; no. 4; p. 73
• Main Authors: Levén, William, Broman, Hampus, Besker, Terese, Torkar, Richard
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Bayesian data analysis; broken windows theory; Compilers; Computer Science; controlled experiment; Crime; Criminal investigations; Criminology; Data analysis; Density; Evidence; Experiments; Interpreters; Programming Languages; Programvaruteknik; Software development; Software development tools; Software Engineering; Software Engineering/Programming and Operating Systems; Source code; technical debt; thematic analysis; Vandalism; controlled experiment; Bayesian data analysis; thematic analysis; technical debt; broken windows theory; Software engineering; Broken windows theory; Criminal activities; Computer software reusability; Thematic analysis; Boiling water reactors; Controlled experiment; Software design; Technical debts; Software development projects; Suboptimal solution; Causal relationships; Software-systems
• ISSN: 1382-3256; 1573-7616; 1573-7616
• DOI: 10.1007/s10664-024-10456-6

Context: The term technical debt (TD) describes the aggregation of sub-optimal solutions that serve to impede the evolution and maintenance of a system. Some claim that the broken windows theory (BWT), a concept borrowed from criminology, also applies to software development projects. The theory states that the presence of indications of previous crime (such as a broken window) will increase the likelihood of further criminal activity; TD could be considered the broken windows of software systems. Objective: To empirically investigate the causal relationship between the TD density of a system and the propensity of developers to introduce new TD during the extension of that system. Method: The study used a mixed-methods research strategy consisting of a controlled experiment with an accompanying survey and follow-up interviews. The experiment had a total of 29 developers of varying experience levels completing system extension tasks in already existing systems with high or low TD density. Results: The analysis revealed significant effects of TD level on the subjects’ tendency to re-implement (rather than reuse) functionality, choose non-descriptive variable names, and introduce other code smells identified by the software tool SonarQube, all with at least 95 % credible intervals. Coclusions: Three separate significant results along with a validating qualitative result combine to form substantial evidence of the BWT’s existence in software engineering contexts. This study finds that existing TD can have a major impact on developers propensity to introduce new TD of various types during development.