Towards surgically-precise technical debt estimation: early results and research roadmap

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

Saarimäki

Taibi

2019

Self Cite

Technical Debt analysis is increasing in popularity as nowadays researchers and industry are adopting various tools for static code analysis to evaluate the quality of their code. Despite this, empirical studies on software projects are expensive because of the time needed to analyze the projects. In addition, the results are difficult to compare as studies commonly consider different projects. In this work, we propose the Technical Debt Dataset, a curated set of project measurement data from 33 Java projects from the Apache Software Foundation. In the Technical Debt Dataset, we analyzed all commits from separately defined time frames with SonarQube to collect Technical Debt information and with Ptidej to detect code smells. Moreover, we extracted all available commit information from the git logs, the refactoring applied with Refactoring Miner, and fault information reported in the issue trackers (Jira). Using this information, we executed the SZZ algorithm to identify the fault-inducing and -fixing commits. We analyzed 78K commits from the selected 33 projects, detecting 1.8M SonarQube issues, 38K code smells, 28K faults and 57K refactorings. The project analysis took more than 200 days. In this paper, we describe the data retrieval pipeline together with the tools used for the analysis. The dataset is made available through CSV files and an SQLite database to facilitate queries on the data. The Technical Debt Dataset aims to open up diverse opportunities for Technical Debt research, enabling researchers to compare results on common projects.

Section: Introductionmentioning

confidence: 99%

The Technical Debt Dataset

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

Saarimäki

Taibi

2019

Self Cite

“…SonarQube TD prediction was also investigated in order to understand whether its calculated TD could be derived from the other metrics that SonarQube measured and not involved in the computation. Unfortunately, the current software metrics do not predict TD, and that TD does not seem to have a large impact on the lead time to add functionalities and fix bugs [14].…”

Section: Related Workmentioning

confidence: 99%

How long do Junior Developers take to Remove Technical Debt Items?

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

Mandić

Katin

et al. 2020

Self Cite

Background. Software engineering is one of the engineering fields with the highest inflow of junior engineers. Tools that utilize source code analysis to provide feedback on internal software quality, i.e. Technical Debt (TD), are valuable to junior developers who can learn and improve their coding skills with minimal consultations with senior colleagues. Objective. We aim at understating which SonarQube TD items junior developers prioritize during the refactoring and how long they take to refactor them. Method. We designed a case study with replicated design and we conducted it with 185 junior developers in two countries, that developed 23 projects with different programming languages and architectures. Results. Junior developers focus homogeneously on different types of TD items. Moreover, they can refactor items in a fraction of the estimated time, never spending more than 50% of the time estimated by SonarQube. Conclusion. Junior Developers appreciate the usage of SonarQube and considered as a useful tool. Companies might ask junior developers to quickly clean their code.

“…Valentina Lenarduzzi aimed at surgically precise technical debt estimation [4]. Starting from the observation that existing metrics used by SonarQube are too coarse-grained and are not taking into account development efforts and historical data, she therefore offered to use them in ML-based technical debt estimation techniques.…”

Section: Quality Attributesmentioning

confidence: 99%

MaLTeSQuE 2021 Workshop Summary

Feitosa

Catolino

SIGSOFT Softw. Eng. Notes

et al. 2022

Self Cite

The 5th edition of the Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2021) workshop was held virtually (originally in Athens, Greece) on August 23rd, 2021, colocated with the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). MaLTeSQuE received a total of ten submissions from all over the world, from which six papers and three abstracts were included in the program. The program also featured a keynote by Tim Menzies on the future of machine learning techniques for software quality. This report summarizes the event and insights stemmed from the keynote and presentations in the three sessions of the workshop.