Comparing Static Analysis and Code Smells as Defect Predictors: An Empirical Study - Open Source Systems 17th IFIP WG 2.13 International Conference, OSS 2021 Virtual Event, May 12–13, 2021 Access content directly
Conference Papers Year : 2021

Comparing Static Analysis and Code Smells as Defect Predictors: An Empirical Study

Abstract

Background. Industrial software increasingly relies on open source software. Therefore, industrial practitioners need to evaluate the quality of a specific open source product they are considering for adoption. Automated tools greatly help assess open source software quality, by reducing the related costs, but do not provide perfectly reliable indications. Indications from tools can be used to restrict and focus manual code inspections, which are typically expensive and time-consuming, only on the code sections most likely to contain faults. Aim. We investigate the extent of the effectiveness of static analysis bug detectors by themselves and in combination with code smell detectors in guiding inspections. Method. We performed an empirical study, in which we used a bug detector (SpotBugs) and a code smell detector (JDeodorant). Results. Our results show that the selected bug detector is precise enough to justify inspecting the code it flags as possibly buggy. Applying the considered code smell detector makes predictions even more precise, but at the price of a rather low recall. Conclusions. Using the considered tools as inspection drivers proved quite useful. The relatively small size of our study does not allow us to draw universally valid conclusions, but our results should be applicable to source code of any kind, although they were obtained from open source code.
Fichier principal
Vignette du fichier
OSS2021_Paper18_CR.pdf (234.53 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03254066 , version 1 (08-06-2021)

Licence

Attribution

Identifiers

Cite

Luigi Lavazza, Sandro Morasca, Davide Tosi. Comparing Static Analysis and Code Smells as Defect Predictors: An Empirical Study. 17th IFIP International Conference on Open Source Systems (OSS), May 2021, Lathi/virtual event, Finland. pp.1-15, ⟨10.1007/978-3-030-75251-4_1⟩. ⟨hal-03254066⟩
115 View
356 Download

Altmetric

Share

Gmail Facebook X LinkedIn More