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AI-Based Software Defect Prediction for Trustworthy Android Apps

Published: 13 June 2022 Publication History

Abstract

The present time in the industry is a time where Android Applications are in a wide range with its widespread of the users also. With the increased use of Android applications, the defects in the Android context have also been increasing. The malware of defective software can be any pernicious program with malignant effects. Many techniques based on static, dynamic, and hybrid approaches have been proposed with the combination of Machine learning (ML) or Artificial Intelligence (AI) techniques. In this regard. Scientifically, it is complicated to examine the malignant effects. A single approach cannot predict defects alone, so multiple approaches must be used simultaneously. However, the proposed techniques do not describe the types of defects they address. The paper aims to propose a framework that classifies the defects. The Artificial Intelligence (AI) techniques are described, and the different defects are mapped to them. The mapping of defects to AI techniques is based on the types of defects found in the Android Context. The accuracy of the techniques and the working criteria has been set as the mapping metrics. This will significantly improve the quality and testing of the product. However, the appropriate technique for a particular type of defect could be easily selected. This will reduce the cost and time efforts put into predicting defects.

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Cited By

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  • (2023)Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile appsConcurrency and Computation: Practice and Experience10.1002/cpe.766436:10Online publication date: 23-Feb-2023

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cover image ACM Other conferences
EASE '22: Proceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering
June 2022
466 pages
ISBN:9781450396134
DOI:10.1145/3530019
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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Association for Computing Machinery

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Publication History

Published: 13 June 2022

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Author Tags

  1. Artificial Intelligence
  2. Defect Prediction Technique
  3. Machine Learning
  4. Software Defect prevention technique

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EASE 2022

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Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

View all
  • (2023)Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile appsConcurrency and Computation: Practice and Experience10.1002/cpe.766436:10Online publication date: 23-Feb-2023

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