An Empirical Investigation of Filter Attribute Selection Techniques for Software Quality Classification

Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs. It has been shown in some studies that prediction accuracy of the models improves when irrelevant and redundant features are removed from the original data set. In this study, we investigated four filter attribute selection techniques, Automatic Hybrid Search (AHS), Rough Sets (RS), Kolmogorov-Smirnov (KS) and Probabilistic Search (PS) and conducted the experiments by using them on a very large telecommunications software system. In order to evaluate their classification performance on the smaller subsets of attributes selected using different approaches, we built several classification models using five different classifiers. The empirical results demonstrated that by applying an attribution selection approach we can build classification models with an accuracy comparable to that built with a complete set of attributes. Moreover, the smaller subset of attributes has less than 15 percent of the complete set of attributes. Therefore, the metrics collection, model calibration, model validation, and model evaluation times of future software development efforts of similar systems can be significantly reduced. In addition, we demonstrated that our recently proposed attribute selection technique, KS, outperformed the other three attribute selection techniques

A Comparative Study of Threshold-based Feature Selection Techniques

Abstract Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The experiments demonstrate that the choice of a performance metric may significantly influence the results. In this study, we have found four distinct patterns when utilizing eight performance metrics to order 11 threshold-based feature selection techniques. Moreover, performances of the software quality models either improve or remain unchanged despite the removal of over 96% of the software metrics (attributes)

High-Dimensional Software Engineering Data and Feature Selection

Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics – we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) and our proposed hybrid feature selection (HFS) technique. Our case study consists of a very highdimensional (42 software attributes) software measurement data set obtained from a large telecommunications system. The empirical analysis indicates that HFS performs better than FRT; however, the Kolmogorov-Smirnov feature ranking technique demonstrates competitive performance. For the telecommunications system, it is found that only 10% of the software attributes are sufficient for effective software quality prediction

Mining Data from Multiple Software Development Projects

A large system often goes through multiple software project development cycles, in part due to changes in operation and development environments. For example, rapid turnover of the development team between releases can influence software quality, making it important to mine software project data over multiple system releases when building defect predictors. Data collection of software attributes are often conducted independent of the quality improvement goals, leading to the availability of a large number of attributes for analysis. Given the problems associated with variations in development process, data collection, and quality goals from one release to another emphasizes the importance of selecting a best-set of software attributes for software quality prediction. Moreover, it is intuitive to remove attributes that do not add to, or have an adverse effect on, the knowledge of the consequent model. Based on real-world software projects’ data, we present a large case study that compares wrapper-based feature ranking techniques (WRT) and our proposed hybrid feature selection (HFS) technique. The comparison is done using both threefold cross-validation (CV) and three-fold cross-validation with risk impact (CVR). It is shown that HFS is better than WRT, while CV is superior to CVR

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection

[Sabbatical Report]

My sabbatical leave was conducted during Spring semester 2014. The leave was successful because it strengthened my research in data mining and software engineering domains and resulted four full-paper publications in peer-reviewed international conferences and one journal paper (to be submitted to a peer-reviewed journal). The purpose of my sabbatical was to complete two main projects: (1) Investigate the stability and defect prediction model performance of feature selection techniques together on real-world software metrics data and (2) Design a novel, robust, and efficient metric selection method for imbalanced data

Bibliography

Bibliography of publications by Huanjing Wang

A Comparative Study of Filter-based Feature Ranking Techniques

One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can capture only one specific aspect of the classification performance, it may be unable to evaluate the classification performance from different perspectives. Also, there is no general consensus among researchers and practitioners regarding which performance metrics should be used for evaluating classification performance. In this study, we investigated six filter-based feature ranking techniques and built classification models using five different classifiers. The models were evaluated using eight different performance metrics. All experiments were conducted on four imbalanced data sets from a telecommunications software system. The experimental results demonstrate that the choice of a performance metric may significantly influence the classification evaluation conclusion. For example, one ranker may outperform another when using a given performance metric, but for a different performance metric the results may be reversed. In this study, we have found five distinct patterns when utilizing eight performance metrics to order six feature selection techniques

Measuring Stability of Threshold-based Feature Selection Techniques

Feature selection has been applied in many domains, such as text mining and software engineering. Ideally a feature selection technique should produce consistent out- puts regardless of minor variations in the input data. Re- searchers have recently begun to examine the stability (robustness) of feature selection techniques. The stability of a feature selection method is defined as the degree of agreement between its outputs to randomly-selected subsets of the same input data. This study evaluated the stability of 11 threshold-based feature ranking techniques (rankers) when applied to 16 real-world software measurement datasets of different sizes. Experimental results demonstrate that AUC (Area Under the Receiver Operating Characteristic Curve) and PRC (Area Under the Precision-Recall Curve) per- formed best among the 11 rankers