ESEIW 2022
Sun 18 - Fri 23 September 2022 Helsinki, Finland
Thu 22 Sep 2022 13:30 - 13:50 at Sonck - Session 2B - Technical Debt & Effort Estimation Chair(s): Carolyn Seaman

Background: Numerous methodologies have been used to study technical debt. Among different data sources, questions and answer sites provide an opportunity to study how users reference and request support on technical debt. To date only a handful of studies, focusing on narrow aspects of technical debt, investigate the phenomenon through the lens of Stack Overflow.

Aims: We aim at gaining an in-depth understanding on the characteristics of technical debt questions of one of the most popular programming question and answer sites, namely Stack Overflow. In addition, we assess if identification strategies based on natural language processing and machine learning models, rather than the keyword-based ones used so far, can be leveraged to automatically identify and classify questions regarding technical debt on the platform.

Method: We use combination of automated and manual processes to identify technical debt questions on Stack Overflow. The final set of 415 questions is analyzed both quantitatively and qualitatively to study (i) technical debt types, (ii) question length, (iii) perceived urgency, (iv) sentiment, and (v) emerging themes. Natural language processing and machine learning techniques are used to evaluate if technical debt questions can be identified and classified automatically.

Results: Architecture debt is the most recurring debt type, followed by code and design debt. Most questions display mild urgency, with the frequency of higher urgency steadily declining as urgency rises. Question length varies across debt types. Sentiment of questions is mostly neutral. 29 themes emerge, with technical debt resolution and management being most recurrent. Machine learning models can be used to automatically identify technical debt questions and binary urgency accurately, but not debt types.

Conclusions: Different patterns emerge from the analysis of technical debt questions on Stack Overflow. The results provide further insights on the phenomenon, and support the adoption of a more comprehensive strategy to identify technical debt questions.

Thu 22 Sep

Displayed time zone: Athens change

13:30 - 15:00
Session 2B - Technical Debt & Effort EstimationESEM Industry Forum / ESEM Emerging Results and Vision Papers / ESEM Technical Papers at Sonck
Chair(s): Carolyn Seaman University of Maryland Baltimore County
Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack Overflow
ESEM Technical Papers
Nicholas Kozanidis Vrije Universiteit Amsterdam, Roberto Verdecchia Vrije Universiteit Amsterdam, Emitzá Guzmán Vrije Universiteit Amsterdam
Vision and Emerging Results
An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned
ESEM Emerging Results and Vision Papers
Shubhashis Karmakar University of Saskatchewan, Zadia Codabux University of Saskatchewan, Melina Vidoni Australian National University
Bayesian Analysis of Bug-Fixing Time using Report Data
ESEM Technical Papers
Renan Vieira Federal University of Ceará, Diego Mesquita Getulio Vargas Foundation, César Lincoln Mattos Federal University of Ceará, Ricardo Britto Ericsson / Blekinge Institute of Technology, Lincoln Souza Rocha Federal University of Ceará, João Gomes Federal University of Ceará
Investigating a NASA Cyclomatic Complexity Policy on Maintenance of a Critical System
ESEM Industry Forum
Daniel Port University of Hawai‘i at Mānoa, Bill Taber Jet Propulsion Laboratory
Vision and Emerging Results
An Empirical Study on the Occurrences of Code Smells in Open Source and Industrial Projects
ESEM Emerging Results and Vision Papers
Md. Masudur Rahman Institute of Information Technology (IIT), University of Dhaka, Abdus Satter University of Dhaka, Mahbubul Alam Joarder Institute of Information Technology (IIT), University of Dhaka, Kazi Sakib Institute of Information Technology, University of Dhaka
DOI Media Attached