Software Defect Prediction is an important activity used in the Testing Phase of the software development life cycle. Within the research of new defect prediction approaches and the selection of training sets for the classification task, different benchmarks have been analyzed in the literature. They provide several features and defective information over specific software archives. Therefore, they are commonly used in research to evaluate new approaches. However, the current benchmarks contain several limitations, such as lack of project variability, outdated benchmarks, single-version projects, a small number of projects and metrics, unavailable resources, poor usability, and non-extensible tools. Therefore, we introduce a novel tool Bgu rEpository mlning foR bUg predicIion (BEIRUT) for benchmark generation for defect prediction, composed of three main features: Given an open-source repository from GitHub, BEIRUT mines the software repository by (1) selecting the best$k$versions, based on the defective rate of each version, (2) generating training sets and a testing set for defect prediction, composed of a large number of metrics and defective information extracted from each of the selected versions and (3) creating defect prediction models from those extracted metrics. In the end, BEIRUT extracts a diversified catalog of 644 metrics and the defective information from each component of$k$versions, automatically selected based on the rate of defects in each version. They were collected from 512 different projects, starting from 2009. The tool is also supplemented with an easy-to-use web interface that provides a configurable selection of projects and metrics and an interface to manage the defect prediction tasks. Moreover, this tool is adapted to be extended with new projects and new extractors, introducing new metrics to the benchmark. The web service tool can be found at rps.ise.bgu.ac.il/beirut.

In this paper, we present the DeBGUer tool, a web-based tool for prediction and isolation of software bugs. DeBGUer is a partial implementation of the Learn, Diagnose, and Plan (LDP) paradigm, which is a recently introduced paradigm for integrating Artificial Intelligence (AI) in the software bug detection and correction process. In LDP, a diagnosis (DX) algorithm is used to suggest possible explanations – diagnoses – for an observed bug. If needed, a test planning algorithm is subsequently used to suggest further testing. Both diagnosis and test planning algorithms consider a fault prediction model, which associates each software component (e.g., class or method) with the likelihood that it contains a bug. DeBGUer implements the first two components of LDP, bug prediction (Learn) and bug diagnosis (Diagnose). It provides an easy-to-use web interface, and has been successfully tested on 12 projects.

Software fault prediction algorithms predict which software components is likely to contain faults using machine learning techniques. Software diagnosis algorithm identify the faulty software components that caused a failure using model-based or spectrum based approaches. We show how software fault prediction algorithms can be used to improve software diagnosis. The resulting data-augmented diagnosis algorithm overcomes key problems in software diagnosis algorithms: ranking diagnoses and distinguishing between diagnoses with high probability and low probability. We demonstrate the efficiency of the proposed approach empirically on three open sources domains, showing significant increase in accuracy of diagnosis and efficiency of troubleshooting. These encouraging results suggests broader use of data-driven methods to complement and improve existing model-based methods.