HackerPapers

arXiv:1807.02971cs.SE

A Longitudinal Cohort Study on the Retainment of Test-Driven Development

Davide Fucci, Simone Romano, Maria Teresa Baldassarre, Danilo Caivano, Giuseppe Scanniello, Burak Thuran, Natalia Juristo

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Background: Test-Driven Development (TDD) is an agile software development practice, which is claimed to boost both external quality of software products and developers' productivity. Aims: We want to study (i) the TDD effects on the external quality of software products as well as the developers' productivity, and (ii) the retainment of TDD over a period of five months. Method: We conducted a (quantitative) longitudinal cohort study with 30 third year undergraduate students in Computer Science at the University of Bari in Italy. Results: The use of TDD has a statistically significant effect neither on the external quality of software products nor on the developers' productivity. However, we observed that participants using TDD produced significantly more tests than those applying a non-TDD development process and that the retainment of TDD is particularly noticeable in the amount of tests written. Conclusions: Our results should encourage software companies to adopt TDD because who practices TDD tends to write more tests---having more tests can come in handy when testing software systems or localizing faults---and it seems that novice developers retain TDD.

arXiv comments
ESEM, October 2018, Oulu, Finland
Journal reference
DOI
Subjects
cs.SESoftware Engineering

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Jul 2018 to Jul 2018. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.