Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1550
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dc.contributor.authorZozas, Ioannis-
dc.contributor.authorBibi, Stamatia-
dc.contributor.authorAmpatzoglou, Apostolos-
dc.date.accessioned2022-11-24T19:00:54Z-
dc.date.available2022-11-24T19:00:54Z-
dc.date.issued2022-
dc.identifier10.1109/TSE.2022.3222318en_US
dc.identifier.issn0098-5589en_US
dc.identifier.issn1939-3520en_US
dc.identifier.issn2326-3881en_US
dc.identifier.urihttps://doi.org/10.1109/TSE.2022.3222318en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1550-
dc.description.abstractJavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.en_US
dc.language.isoenen_US
dc.sourceIEEE Transactions on Software Engineeringen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherSoftware Qualityen_US
dc.subject.otherJavaScripten_US
dc.subject.otherCode Technical Debten_US
dc.subject.otherSource Code Qualityen_US
dc.titleForecasting the Principal of Code Technical Debt in JavaScript Applicationsen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.firstpage1en_US
local.identifier.lastpage15en_US
Appears in Collections:Department of Applied Informatics

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