Different from within-project software defect prediction (WPDP), cross-project software defect prediction (CPDP) does not require sufficient training data and can help developers in the early stages of software development. Recent studies tried to learn semantic features for CPDP by feeding neural networks with abstract syntax tree (AST) token vectors. However, the ASTs directly parsed from software modules usually have complex structures, which are reflected on more nodes and deeper size, and the transfer learning is not regularly adopted to further reduce the data distribution difference between the source project and the target project. To solve these problems, we aim to joint learn the statement level trees (SLT) and alleviate data distribution difference with maximum mean discrepancy (MMD) to improve defect prediction performance on CPDP. Specifically, we propose a novel cross-project defect prediction method S2LMMD via statement semantic learning and MMD. We first construct the SLT by splitting the original AST on specified node. Then we generate more effective semantic features by learning of sequence embedding with Bi-GRU neural network. Finally, a transfer loss MMD is carried out to keep more common characteristics across different project datasets to further improve CPDP performance. To verify the effectiveness of our proposed method, we conducted experiments on ten widely used open-source projects and evaluated the experimental performance by using AUC measures. Our empirical results show that our proposed method S2LMMD can significantly outperform eight state-of-the-art baselines. In addition, for semantic learning, SLT has a higher influence on CPDP, while MMD is of great significance in transfer learning.

Unlike code errors, the presence of code smell often does not affect the behavior of the software system, but it will cause quality problems in terms of readability, understandability, and efficiency. To improve the software quality and reduce the maintenance costs, the developers need to detect code smells rapidly and make corresponding code refactoring. In code smell detection, recently, machine learning-based methods become more prevalent and can overcome the shortcomings of the heuristic-based methods, which mainly rely on manually designed rules. However, to our best knowledge, there is little research to analyze whether using hyper-parameter optimization can improve the performance of machine learning-based methods. In this study, we mainly focus on two classical code smells (i.e., Data Class and Feature Envy). First, we consider four optimizers for hyper-parameter optimization, and six commonly used classifiers for machine-learning-based methods. Second, we use AUC as the performance measure to evaluate the performance of constructed models. Based on final empirical results, we find that (1) Using hyper-parameter optimization can significantly improve the performance of code smell detection. (2) Differential evolution (DE) optimizer can achieve better performance than the other three optimizers when using the random forest classifier. (3) We can further improve the performance of code smell detection when performing parameter optimization on the DE optimizer.

Cross project defect prediction (CPDP) is a challenging task since the predictor built on the source projects can hardly generalize well to the target project. Previous studies have shown that both feature mapping and feature selection can alleviate the differences between the source and target projects. In this paper, we propose a novel method FeSCH (Feature Selection using Clusters of Hybrid-data). In particular it includes two phases. The first is the feature clustering phase, which uses a density-based clustering method DPC to group highly co-related features into clusters. The second is the feature selection phase, which selects beneficial features from each cluster. We design three ranking strategies to choose appropriate features. During the empirical studies, we design experiments based on real-world software projects, and evaluate the prediction performance of FeSCH by analyzing the influence of ranking strategies. The experimental results show that FeSCH can outperform three baseline methods (i.e., WPDP, ALL, and TCA+) in most cases, and its performance is independent of the used classifiers.

Software fault prediction is a valuable exercise in software quality assurance to best allocate limited testing resources. Classification is one of the effective methods for software fault prediction. The classification models are trained based on the datasets obtained by mining software historical repositories. However, the performance of the models depends on the quality of datasets. In this paper, we propose a novel two-stage data preprocessing approach which incorporates both feature selection and instance reduction. Specifically, in the feature selection stage, we first perform relevance analysis, and then propose a threshold-based clustering method, called novel threshold-based clustering algorithm, to conduct redundancy control. In the instance reduction stage, we apply random under-sampling to keep the balance between the faulty and non-faulty instances. In empirical studies, we chose datasets from real-world software projects, such as Eclipse and NASA. Then we compared our approach with some classical baseline methods, and further investigated the influencing factors in our approach. The final results demonstrate the effectiveness of our approach, and provide a guideline for achieving cost-effective data preprocessing when using our two-stage approach.

Noises are inevitable when mining software archives for software fault prediction. Although some researchers have investigated the noise tolerance of existing feature selection methods, few studies focus on proposing new feature selection methods with a certain noise tolerance. To solve this issue, we propose a novel method FECS (FEature Clustering with Selection strategies). This method includes two phases: a feature clustering phase and a feature selection phase with three different heuristic search strategies. During empirical studies, we choose real-world software projects, such as Eclipse and NASA and inject class level and feature level noises simultaneously to imitate noisy datasets. After using classical feature selection methods as the baseline, we confirm the effectiveness of FECS and provide a guideline of using FECS after analyzing the effects of varying either the percentage of selected features or the noise rate.

Software defect prediction can classify new software entities into either buggy or clean. However the effectiveness of existing methods is influenced by irrelevant and redundant features. In this paper, we propose a new feature selection framework FECAR using Feature Clustering And feature Ranking. This framework firstly partitions original features into k clusters based on FF-Correlation measure. Then it selects relevant features from each cluster based on FC-Relevance measure. In empirical study, we choose Symmetric Uncertainty as FF-Correlation measure, and choose Information Gain, Chi-Square, and Relief as three different FC-Relevance measures. Based on some real projects Eclipse and NASA, we implemented our framework and performed empirical studies to investigate the redundancy rate and the performance of the trained defect predictors. Final results verify the effectiveness of our proposed framework and further provide a guideline for achieving cost-effective feature selection when using our framework.