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A novel search-based approach to software quality modeling with multiple software project repositories is presented. Training a software quality model with only one software measurement and defect data set may not effectively encapsulate quality trends of the development organization. The inclusion of additional software projects during the training process(More)
The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better(More)
Current software quality estimation models often involve using supervised learning methods to train a software quality classifier or a software fault prediction model. In such models, the dependent variable is a software quality measurement indicating the quality of a software module by either a risk-based class membership (e.g., whether it is fault-prone(More)
The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application(More)
High-assurance and complex mission-critical software systems are heavily dependent on reliability of their underlying software applications. An early software fault prediction is a proven technique in achieving high software reliability. Prediction models based on software metrics can predict number of faults in software modules. Timely predictions of such(More)
We addresses the important problem of software quality analysis when there is limited software fault or fault-proneness data. A software quality model is typically trained using software measurement and fault data obtained from a previous release or similar project. Such an approach assumes that fault data is available for all the training modules. Various(More)
Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical(More)
For software quality estimation, software development practitioners typically construct quality-classification or fault prediction models using software metrics and fault data from a previous system release or a similar software project. Engineers then use these models to predict the fault proneness of software modules in development. Software quality(More)
Software metrics-based quality estimation models can be effective tools for identifying which modules are likely to be fault-prone or not fault-prone. The use of such models prior to system deployment can considerably reduce the likelihood of faults discovered during operations, hence improving system reliability. A software quality classification model is(More)