AI-Based Software Defect Prediction for Trustworthy Android Apps

sadaf, saadia; Iqbal, Danish; Bühnová, Barbora

doi:10.1145/3530019.3531330

Cited by 1 publication

(2 citation statements)

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“…To solve this, previous studies began to focus on software defect prediction techniques, 8‐13 which to predict whether code blocks have defects by building models based on machine learning technology. Software defect prediction is a hot research topic for mobile software quality assurance 14‐19 …”

Section: Introductionmentioning

confidence: 99%

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Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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SummaryWith the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.

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Section: Introductionmentioning

confidence: 99%

“…Software defect prediction is a hot research topic for mobile software quality assurance. [14][15][16][17][18][19]…”

mentioning

confidence: 99%