Concordia University


Master Thesis Defense: Giancarlo Sierra Monge

January 29, 2019

Speaker: Giancarlo Sierra Monge

Supervisor: Dr. E. Shihab

Examining Committee:
Drs. T.-H. Chen, Y.-G. Gueheneuc, D. Pankratov (Chair)

Title: Towards the Repayment of Self-admitted Technical Debt

Date: Tuesday, January 29, 2019

Time: 14:00

Place: EV 3.309


Technical Debt is a metaphor used to express sub-optimal source code implementations that are introduced for short-term benefits that often need to be paid back later, at an increased cost. In recent years, various empirical studies have focused on investigating source code comments that indicate Technical Debt, often referred to as Self-Admitted Technical Debt (SATD).

In this thesis, we survey research work on SATD, analyzing characteristics of current approaches and techniques for SATD, diving literature in three: detection, comprehension, and repayment. To set the stage for novel and improved work on SATD, we compile tools, resources, and data sets made publicly available. We also identify areas that are missing investigation, open challenges, and discuss potential future research avenues. From the literature survey, we conclude that most findings and contributions have focused on techniques to identify, classify and comprehend SATD. However, few studies focused on the repayment or management of SATD, which is an essential goal of studying technical debt for software maintenance.

Therefore, we perform an empirical study towards SATD repayment. We conducted a preliminary online survey with developers to understand the elements they consider to prioritize SATD. With the acquired knowledge from the survey responses and previous literature work, we select metrics to estimate SATD repayment effort. We examine SATD instances found in software systems to see how it has been repaid and investigate the possibility of using historical data at the time of SATD introduction as indicators for SATD that should be addressed. We find two SATD repayment effort metrics that can be consistently modeled in our studied projects and surface the best early indicators for important SATD.

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