Using analytics to quantify the interest of self-admitted technical debt

Yasutaka Kamei, Everton Maldonado, Emad Shihab, Naoyasu Ubayashi

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

Technical debt refers to the phenomena of taking a shortcut to achieve short term development gain at the cost of increased maintenance effort in the future. The concept of debt, in particular, the cost of debt has not been widely studied. Therefore, the goal of this paper is to determine ways to measure the 'interest' on the debt and use these measures to see how much of the technical debt incurs positive interest, i.e., debt that indeed costs more to pay off in the future. To measure interest, we use the LOC and Fan-In measures. We perform a case study on the Apache JMeter project and find that approximately 42-44% of the technical debt incurs positive interest.

Original languageEnglish
Pages (from-to)68-71
Number of pages4
JournalCEUR Workshop Proceedings
Volume1771
Publication statusPublished - Jan 1 2016
EventJoint of the 4th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2016 and 1st International Workshop on Technical Debt Analytics, TDA 2016 - Hamilton, New Zealand
Duration: Dec 6 2016 → …

Fingerprint

Costs

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Using analytics to quantify the interest of self-admitted technical debt. / Kamei, Yasutaka; Maldonado, Everton; Shihab, Emad; Ubayashi, Naoyasu.

In: CEUR Workshop Proceedings, Vol. 1771, 01.01.2016, p. 68-71.

Research output: Contribution to journalConference article

@article{33b67fec4e9d41fda808176d610f1fa5,
title = "Using analytics to quantify the interest of self-admitted technical debt",
abstract = "Technical debt refers to the phenomena of taking a shortcut to achieve short term development gain at the cost of increased maintenance effort in the future. The concept of debt, in particular, the cost of debt has not been widely studied. Therefore, the goal of this paper is to determine ways to measure the 'interest' on the debt and use these measures to see how much of the technical debt incurs positive interest, i.e., debt that indeed costs more to pay off in the future. To measure interest, we use the LOC and Fan-In measures. We perform a case study on the Apache JMeter project and find that approximately 42-44{\%} of the technical debt incurs positive interest.",
author = "Yasutaka Kamei and Everton Maldonado and Emad Shihab and Naoyasu Ubayashi",
year = "2016",
month = "1",
day = "1",
language = "English",
volume = "1771",
pages = "68--71",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",

}

TY - JOUR

T1 - Using analytics to quantify the interest of self-admitted technical debt

AU - Kamei, Yasutaka

AU - Maldonado, Everton

AU - Shihab, Emad

AU - Ubayashi, Naoyasu

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Technical debt refers to the phenomena of taking a shortcut to achieve short term development gain at the cost of increased maintenance effort in the future. The concept of debt, in particular, the cost of debt has not been widely studied. Therefore, the goal of this paper is to determine ways to measure the 'interest' on the debt and use these measures to see how much of the technical debt incurs positive interest, i.e., debt that indeed costs more to pay off in the future. To measure interest, we use the LOC and Fan-In measures. We perform a case study on the Apache JMeter project and find that approximately 42-44% of the technical debt incurs positive interest.

AB - Technical debt refers to the phenomena of taking a shortcut to achieve short term development gain at the cost of increased maintenance effort in the future. The concept of debt, in particular, the cost of debt has not been widely studied. Therefore, the goal of this paper is to determine ways to measure the 'interest' on the debt and use these measures to see how much of the technical debt incurs positive interest, i.e., debt that indeed costs more to pay off in the future. To measure interest, we use the LOC and Fan-In measures. We perform a case study on the Apache JMeter project and find that approximately 42-44% of the technical debt incurs positive interest.

UR - http://www.scopus.com/inward/record.url?scp=85009495604&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85009495604&partnerID=8YFLogxK

M3 - Conference article

VL - 1771

SP - 68

EP - 71

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -