Effort-aware cross-project just-in-time defect prediction framework for mobile apps

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.

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“…They used adversarial learning and experimented on 14 Android applications. This model gives better results when compared with 20 others [21].…”

Section: Background and Related Workmentioning

confidence: 74%

Deep Learning-Based Defect Prediction for Mobile Applications

Jorayeva

Akbulut

Çatal

et al. 2022

Sensors

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“…Most previous EADP studies [13,[16][17][18][19][20][21] usually built predictive models on historical labelled software modules and then predicted the defect-proneness of unlabelled modules in the same project, referred to as within-project EADP. However, in actual software development scenarios, it is hard to obtain a large amount of historical data from the same project, especially for newly developed software [22][23][24][25][26][27]. Therefore, Ni et al [28] proposed an effort-aware cross-project defect prediction (EACPDP) method called EASC in their IEEE transactions on software engineering (TSE) paper titled 'Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction'.…”

Section: Motivationsmentioning

confidence: 99%

“…Carka et al [67] proposed to evaluate the EADP performance using normalised PofB, which ranked software modules based on predicted defect densities. Zhao et al [21], Xu et al [68] and Cheng et al [22] proposed three JIT EADP methods for Android applications. Ni et al [28] proposed a supervised EACPDP method called EASC.…”

Section: Effort-aware Defect Predictionmentioning

confidence: 99%

“…Most previous EADP studies [13, 16–21] usually built predictive models on historical labelled software modules and then predicted the defect‐proneness of unlabelled modules in the same project, referred to as within‐project EADP. However, in actual software development scenarios, it is hard to obtain a large amount of historical data from the same project, especially for newly developed software [22–27]. Therefore, Ni et al.…”

Section: Introductionmentioning

confidence: 99%

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Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

Yang

Keung

et al. 2023

IET Software

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Effort-aware cross-project defect prediction (EACPDP), which uses cross-project software modules to build a model to rank within-project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross-project modules to train a model without considering the data distribution difference between cross-project and within-project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort-aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K-nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model. K E Y W O R D Sdata mining, quality assurance, software engineering, software maintenance, software metrics, software quality | INTRODUCTIONComputer software is widely used in various industries in society today, which may fail with quality problems. As software plays an increasingly important role in various fields, ensuring software reliability is one of the issues people are more concerned about [1][2][3]. Software defects are a potential sources of errors, failures, and crashes of associated systems. However, the increase in the scale of the software makes defect inspection and fixing more time-consuming. Worse yet, softwareThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Their empirical research results showed that (1) in terms of machine learning algorithms, the classification performance of Naive Bayes is better than that of logistic regression, decision table, and support vector machine; (2) in terms of ensemble methods, boosting is better than random forests, voting and bootstrap aggregating. Recently, Cheng et al proposed the KAL 23 method for effort-aware JIT defect prediction, they first transformed original features into a high-dimensional feature space and adopted an adversarial learning technique to extract the common features. Experimental results showed that the KAL method achieved better performance than other comparative methods.…”

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confidence: 99%

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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Effort-aware cross-project just-in-time defect prediction framework for mobile apps

Cited by 13 publications

References 43 publications

Deep Learning-Based Defect Prediction for Mobile Applications

Deep Learning-Based Defect Prediction for Mobile Applications

Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

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

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