Mobile Application Software Defect Prediction

Ricky, Michael Yoseph; Purnomo, Fredy; Yulianto, Budi

doi:10.1109/sose.2016.25

Cited by 13 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In studies that do not employ deep learning techniques, for the most part, a static feature selection that is manually chosen by knowledgeable domain experts is preferred. However, we also observed that the Correlation-based Feature Selection (CFS) method was used in several studies [ 37 , 38 , 39 , 40 , 41 ] as the feature subset selection technique. Secondly, gain ratio attribute evaluation is used [ 42 , 43 , 44 ] to reduce the high-dimensionality and further improve efficiency.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

Jorayeva

Akbulut

Çatal

et al. 2022

Sensors

View full text Add to dashboard Cite

Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

Jorayeva

Akbulut

Çatal

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…They conducted experiments on an open source app with seven machine learning methods and the results showed that process metrics-based models achieved better performance for defect prediction on apps. Ricky et al [45] proposed an SVM method to predict defects on apps. Their experimental results on five datasets showed that their SVM method achieved better performance than that of the decision trees.…”

Section: Defect Prediction For Android Appsmentioning

confidence: 99%

“…Ricky et al. [45] proposed an SVM method to predict defects on apps. Their experimental results on five datasets showed that their SVM method achieved better performance than that of the decision trees.…”

Section: Related Workmentioning

confidence: 99%

A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps

et al. 2021

View full text Add to dashboard Cite

Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just‐in‐Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high‐quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state‐of‐the‐art cost‐sensitive cross‐entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort‐aware performance indicators in most cases.

show abstract

“…Software defect prediction technology is to design software metrics related to software defects by analyzing software code, software development process, etc., and then establish the relationship between software metrics and software defects by using historical defect data. Many technologies based on machine learning have been used to predict software defects, including artificial neural network [7], bayesian network [8], SVM [9], dictionary learning [10], association rule [11], naive bayes [12], tree-based methods [13], evolutionary algorithm [14], etc. However, these algorithms ignore the high dimension and class distribution imbalance of the defect data set, which have a great impact on classification performance [15].…”

Section: Related Workmentioning

confidence: 99%

Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Zhang

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR_SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.

show abstract

Mobile Application Software Defect Prediction

Cited by 13 publications

References 16 publications

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps

Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Contact Info

Product

Resources

About