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Technical Debt Management with Genetic Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
PublisherIEEE
Pages50-53
ISBN (Electronic)978-1-5090-2820-7
DOIs
Publication statusPublished - 31 Aug 2016
Publication typeA4 Article in a conference publication
EventEuromicro Conference on Software Engineering and Advanced Applications -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2376-9505

Conference

ConferenceEuromicro Conference on Software Engineering and Advanced Applications
Period1/01/00 → …

Abstract

Management of technical debt is a challenging and poorly understood task, and it is becoming even harder in the case of modern software engineering practices like Agile development and Continuous Delivery. In this research we assume an agile software development and management process where the organization selects the tasks in the beginning of each sprint. The candidate tasks include implementation of new features with assumed business value and paying back technical debt. The organization needs to select a combination of tasks that is implementable by the available resources and maximize the benefit for the organization. The required optimization problem in a large project is complex and is also a multi-objective problem, which involves trade-off between short-term and long-term value delivered by the software. In this paper, we apply a multiobjective genetic algorithm for solving such an optimization problem. The potential of the algorithm is demonstrated by applying it to a student project.

Publication forum classification

Field of science, Statistics Finland