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Pregled bibliografske jedinice broj: 1107027

Improving Software Defect Prediction by Aggregated Change Metrics


Šikić, Lucija; Afrić, Petar; Kurdija, Adrian Satja; Šilić, Marin
Improving Software Defect Prediction by Aggregated Change Metrics // IEEE access, 9 (2021), 19391-19411 doi:10.1109/ACCESS.2021.3054948 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1107027 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Improving Software Defect Prediction by Aggregated Change Metrics

Autori
Šikić, Lucija ; Afrić, Petar ; Kurdija, Adrian Satja ; Šilić, Marin

Izvornik
IEEE access (2169-3536) 9 (2021); 19391-19411

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
classification, feature engineering, process metrics, change metrics, software defect prediction

Sažetak
To ensure the delivery of high quality software, it is necessary to ensure that all of its artifacts function properly, which is usually done by performing appropriate tests with limited resources. It is therefore desirable to identify defective artifacts so that they can be corrected before the testing process. So far, researchers have proposed various predictive models for this purpose. Such models are typically trained on data representing previous project versions of a software and then used to predict which of the software artifacts in the new version are likely to be defective. However, the data representing a software project usually consists of measurable properties of the project or its modules, and leaves out information about the timeline of the software development process. To fill this gap, we propose a new set of metrics, namely aggregated change metrics, which are created by aggregating the data of all changes made to the software between two versions, taking into account the chronological order of the changes. In experiments conducted on open source projects written in Java, we show that the stability and performance of commonly used classification models are improved by extending a feature set to include both measurable properties of the analyzed software and the aggregated change metrics.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekti:
IP-2018-01-6423 - Pouzdani kompozitni primjenski sustavi zasnovani na web uslugama (RELS) (Srbljić, Siniša, HRZZ - 2018-01) ( POIROT)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Lucija Šikić (autor)

Avatar Url Petar Afrić (autor)

Avatar Url Marin Šilić (autor)

Avatar Url Adrian Satja Kurdija (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Šikić, Lucija; Afrić, Petar; Kurdija, Adrian Satja; Šilić, Marin
Improving Software Defect Prediction by Aggregated Change Metrics // IEEE access, 9 (2021), 19391-19411 doi:10.1109/ACCESS.2021.3054948 (međunarodna recenzija, članak, znanstveni)
Šikić, L., Afrić, P., Kurdija, A. & Šilić, M. (2021) Improving Software Defect Prediction by Aggregated Change Metrics. IEEE access, 9, 19391-19411 doi:10.1109/ACCESS.2021.3054948.
@article{article, year = {2021}, pages = {19391-19411}, DOI = {10.1109/ACCESS.2021.3054948}, keywords = {classification, feature engineering, process metrics, change metrics, software defect prediction}, journal = {IEEE access}, doi = {10.1109/ACCESS.2021.3054948}, volume = {9}, issn = {2169-3536}, title = {Improving Software Defect Prediction by Aggregated Change Metrics}, keyword = {classification, feature engineering, process metrics, change metrics, software defect prediction} }
@article{article, year = {2021}, pages = {19391-19411}, DOI = {10.1109/ACCESS.2021.3054948}, keywords = {classification, feature engineering, process metrics, change metrics, software defect prediction}, journal = {IEEE access}, doi = {10.1109/ACCESS.2021.3054948}, volume = {9}, issn = {2169-3536}, title = {Improving Software Defect Prediction by Aggregated Change Metrics}, keyword = {classification, feature engineering, process metrics, change metrics, software defect prediction} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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