Conference paper Open Access

Technical Debt Quantification through Metrics: An Industrial Validation

Angeliki-Agathi Tsintzira; Areti Ampatzoglou; Oliviu Matei; Apostolos Ampatzoglou; Alexander Chatzigeorgiou; Robert Heb


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    <dct:title>Technical Debt Quantification through Metrics: An Industrial Validation</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>technical debt, industrial, case study, metrics</dcat:keyword>
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    <dct:description>&lt;p&gt;Technical Debt is a software engineering metaphor that refers to the intentional or unintentional production of software at a lower quality, to achieve business goals (e.g., shorten time to market). Nevertheless, similarly to financial debt, technical debt does not come without negative consequences. The accumulation of technical debt leads to additional maintenance. The technical debt metaphor is built around three major concepts: principal, interest, and interest probability. The quantification of these notions is the first step towards the efficient management of technical debt, in the sense that &amp;ldquo;you cannot control what you cannot measure&amp;rdquo;. In this paper, we employ an established method for quantifying technical debt, namely FITTED, to measure the technical debt of an industrial software product, and contrast it to the perception of the software engineers. The main contribution of this work is the validation of FITTED in an industrial setting, and particularly in the Embedded Low Power Systems domain. The results of the study suggest that FITTED is able of accurately ranking software components, with respect to their principal, interest, and interest probability.&lt;/p&gt;</dct:description>
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    <dct:title>Software Development toolKit for Energy optimization and technical Debt elimination</dct:title>
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