Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

IPCP: Immersive Parallel Coordinates Plots for Engineering Design Processes

Computational engineering design methods and tools are common practice in modern industry. Such approaches are integral in enabling designers to efficiently explore larger and more complex design spaces. However, at the same time, computational engineering design methods tend to dramatically increase the number of candidate solutions that decision-makers must interpret in order to make appropriate choices within a set of solutions. Since all candidate solutions can be represented in digital form together with their assessment criteria, evaluated according to some sort of simulation model, a natural way to explore and understand the complexities of the design problem is to visualize their multidimensional nature. The task now involves the discovery of patterns and trends within the multidimensional design space. In this work, we aim to enhance the design decision-making process by embedding visual analytics into an immersive virtual reality environment. To this end, we present a system called IPCP: immersive parallel coordinates plots. IPCP combines the well-established parallel coordinates visualization technique for high-dimensional data with immersive virtual reality. We propose this approach in order to exploit and discover efficient means to use new technology within a conventional decision-making process. The aim is to provide benefits by enhancing visualizations of 3D geometry and other physical quantities with scientific information. We present the design of this system, which allows the representation and exploration of multidimensional scientific datasets. A qualitative evaluation with two surrogate expert users, knowledgeable in multidimensional data analysis, demonstrate that the system can be used successfully to detect both known and previously unknown patterns in a real-world test dataset, producing an early indicative validation of its suitability for decision support in engineering design processes.Cambridge European and Trinity Hall; Engineering and Physical Sciences Research Council (EPSRC-1788814

Visual patterns in issue tracking data

Software development teams gather valuable data about features and bugs in issue tracking systems. This information can be used to measure and improve the efficiency and effectiveness of the development process. In this paper we present an approach that harnesses the extraordinary capability of the human brain to detect visual patterns. We specify generic visual process patterns that can be found in issue tracking data. With these patterns we can analyze information about effort estimation, and the length, and sequence of problem resolution activities. In an industrial case study we apply our interactive tool to identify instances of these patterns and discuss our observations. Our approach was validated through extensive discussions with multiple project managers and developers, as well as feedback from the project review board

Computational design optimization for S-Ducts

In this work, we investigate the computational design of a typical S-Duct that is found in the literature. We model the design problem as a shape optimization study. The design parameters describe the 3D geometrical changes to the shape of the S-Duct and we assess the improvements to the aerodynamic behavior by considering two objective functions: the pressure losses and the swirl. The geometry management is controlled with the Free-Form Deformation (FFD) technique, the analysis of the flow is performed using steady-state computational fluid dynamics (CFD), and the exploration of the design space is achieved using the heuristic optimization algorithm Tabu Search (MOTS). The results reveal potential improvements by 14% with respect to the pressure losses and by 71% with respect to the swirl of the flow. These findings exceed by a large margin the optimality level that was achieved by other approaches in the literature. Further investigation of a range of optimum geometries is performed and reported with a detailed discussion

Large outbreaks of Ips acuminatus in Scots pine stands of the Italian Alps

In the last years, many Scots pine (Pinus sylvestris) stands have been severely attacked by the bark beetle Ips acuminatus (Coleoptera Curculionidae Scolytinae). In the outbreak area of San Vito di Cadore (Eastern Dolomites), the number of attacked trees since 2005 and both the emergence of bark beetles and natural enemies have been assessed. The investigated forests showed dozens of easily recognizable infestation spots with size ranging from about 20-30 trees (small spots) up to 300 trees (large spots). These infested spots evolved quickly, while new ones appeared within a radius of few hundreds of meters. During the last 5 years (2006-2010) we sampled branches from small and large spots and lodged them into emergence cages: adults of I. acuminatus as well as natural enemies were collected weekly, identified and counted. At the same time, a monitoring program of the surveyed pine stands was carried out to check the enlargement of old spots and the appearance of new ones. Voltinism and phenology of I. acuminatus were investigated by pheromone traps baited with different lures (Austrian vs. Spanish lures). The effects of a sanitation felling of about 4500 infested trees, carried out by the Regional Forest Service in autumn 2007 on I. acuminatus population were also assessed. Throughout the whole sampling area I. acuminatus resulted bivoltine, with the highest density attained during the first generation. However, a part of the population still evidenced a monovoltine behaviour. The realized sanitation felling strongly reduced both breeding sites and the number of infested trees observed during the following year. Moreover the pheromone-baited traps gave useful information about changes in bark beetle population density; the trapping efficiency of Spanish lure resulted clearly higher than the Austrian one. Finally, the recorded parasitism may have a role in outbreak dynamics as it was significantly higher during the second host generation, in both small and large spots

Preliminary BCP flowfield investigation by CFD simulations and PIV in a transparent model of a SRF elliptical lowbeta cavity

Standard vertical Buffered Chemical Polishing (BCP) is one of the main surface treatment for Superconducting Radiofrequency (SRF) cavities.A finite element Computational Fluid Dynamic (CFD) model has been developed. Uncertainties in the solution of fluid simulations are not negligible due to the complex geometry of a SRF cavity; thus without an experimental validation, results from this type of simulations cannot be confidently used to improve the process. To this aim, an experimental study was started to investigate the fluid dynamics of the BCP process by means of Particle Image Velocimetry (PIV) technique. Similitude on Reynolds number and Refractive Index Matching (RIM) technique were also implemented to replace the dangerous BCP mixture with a glycerine-water mixture. The paper describes the preliminary results from simulations and experimen