A Microservice-based Platform for Software Technical Debt Analysis

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dc.contributor.advisorNorbisrath, Ulrich, juhendaja
dc.contributor.advisorRossi, Bruno, juhendaja
dc.contributor.authorMulianingtyas, Octanty
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-09-28T08:54:59Z
dc.date.available2023-09-28T08:54:59Z
dc.date.issued2021
dc.description.abstractTechnical debt (TD) is defined as developing a new feature by a poor code and design to fulfil a punctual release date that may provide shorterm advantage, but negatively impact the future. Reducing TD is crucial to improve the software quality and decrease the financial issue. However, TD is cumbersome to calculate since every TD tool provided uses numerous metrics and models. This study aims to provide analysis about technical debt alternative measurements by developing a microservice-based platform on a set of large open-source projects. The result of the analysis is expected can be used as a reference for the researchers to arrange a proper formula to calculate TD. The microservice platform was selected since it has several benefits compared to monolithic applications. The benefits comprised simple deployment, fewer environment dependencies, and maintainability. We implemented three TD identification methods in the microservice platform: Maintainability Index (MI), SIG Maintainability, and Sqale. Through 124 directory java projects run in the microservice platform, the statistical analysis results show no correlation among the three TD identification methods. The increasing value of the metrics of TotalOp, TotalOpr, CC, Duplication, UnitSize, and NumberParameters, WMC will decrease the ranking of result in each TD Identification method, and thus decrease the overall TD. The scalability of the microservice is seen by the performance of the microservice by the total time for a query executed in the API. We used the load testing in the postman to see the total time. The API used for testing employed the aforementioned All TD Identification method. From the experiment, the time for a query executed for calculating 5 directories is 1m 40.38 s and it will continuously improve until 34m 20.70 s for 124 directories. We also tested the microservice platform for running in seven iterations. However, the number of iteration is not correlated with the time for a query executed. The time for a query executed is variated in every iteration and not increasing continuously as the increment of the number of iterations. Based on these facts, the microservice performance is only influenced by the number of directories upload instead of the number of iterations for executing.et
dc.identifier.urihttps://hdl.handle.net/10062/93207
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMicroserviceset
dc.subjectMaintainability Indexet
dc.subjectSIG Maintainabilityet
dc.subjectSqaleet
dc.subjectTechnical Debtet
dc.subjectTechnical Debt Identification Methodet
dc.subject.othermagistritöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titleA Microservice-based Platform for Software Technical Debt Analysiset
dc.typeThesiset

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