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Classification Based Software Defect Prediction Model for Finance Software System - An Industry Study

Published: 28 January 2020 Publication History

Abstract

Automated software defect prediction is an important and fundamental activity in the domain of software development. Successful software defect prediction can save testing effort thus reduce the time and cost for software development. However, software systems for finance company are inherently large and complex with numerous interfaces with other systems. Thus, identifying and selecting a good model and a set of features is important but challenging problem. In our paper, we first define the problem we want to solve. Then we propose a prediction model based on binary classification and a set of novel features, which is more specific for finance software systems. We collected 15 months real production data and labelled it as our dataset. The experiment shows our model and features can give a better prediction accuracy for finance systems. In addition, we demonstrate how our prediction model helps improve our production quality further. Unlike other research papers, our proposal focuses to solve problem in real finance industry.

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  • (2023)Industrial applications of software defect prediction using machine learningInformation and Software Technology10.1016/j.infsof.2023.107192159:COnline publication date: 1-Jul-2023

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cover image ACM Other conferences
ICSEB '19: Proceedings of the 2019 3rd International Conference on Software and e-Business
December 2019
215 pages
ISBN:9781450376495
DOI:10.1145/3374549
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Waseda University: Waseda University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 January 2020

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Author Tags

  1. Faulty change
  2. Finance system
  3. Machine learning
  4. Software defect prediction

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ICSEB 2019

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  • (2023)Industrial applications of software defect prediction using machine learningInformation and Software Technology10.1016/j.infsof.2023.107192159:COnline publication date: 1-Jul-2023

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