The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve prediction performance when the fit dataset is imbalanced, i.e. there exists a large difference between the number of fault-prone modules and not-fault-prone modules. So far, there has been no research reporting the effects of applying sampling methods to fault-proneness models. In this paper, we experimentally evaluated the effects of four sampling methods (random over sampling, synthetic minority over sampling, random under sampling and one-sided selection) applied to four fault-proneness models (linear discriminant analysis, logistic regression analysis, neural network and classification tree) by using two module sets of industry legacy software. All four sampling methods improved the prediction performance of the linear and logistic models, while neural network and classification tree models did not benefit from the sampling methods. The improvements of F1-values in linear and logistic models were 0.078 at minimum, 0.224 at maximum and 0.121 at the mean.
Background: Any software project dataset sometimes includes outliers which affect the accuracy of effort estimation. Outlier deletion methods are often used to eliminate them. However, there are few case studies which apply outlier deletion methods to analogy-based estimation, so it is not clear which method is more suitable for analogy-based estimation. Aim: Clarifying the effects of existing outlier deletion methods (Cook's distance based deletion, LTS based deletion, k-means based deletion, Mantel's correlation based deletion, and EID based deletion) and our method for analogy-based estimation. Method: In the experiment, outlier deletion methods were applied to three kinds of datasets (the ISBSG, Kitchenham, and Desharnais datasets), and their estimation accuracy evaluated based on BRE (Balanced Relative Error). Our method eliminates outliers from the neighborhoods of a target project when the effort is extremely different from other neighborhoods. Results: Deletion methods which are designed to apply to analogy-based estimation (i.e. Mantel's correlation based deletion, EID based deletion, and our method) showed stable performance. Especially, only our method showed over 10% improvement of the average BRE on two datasets. Conclusions: It is reasonable to apply deletion methods designed for analogy-based estimation, and more preferable to apply our method to analogybased estimation.
Software birthmarks are unique and native characteristics of every software component. Two components having similar birthmarks indicate that they are similar in functionality, structure and implementation. Questions addressed in this paper include: Which are similar class files? Can they be gathered into one class file? What are major functionalities among class files? To answer to these questions, this paper analyzed the similarity of birthmarks for all pairs of classes in ArgoUML, and visualized them using Multi-Dimensional Scaling (MDS). As a result, three pairs of very similar class files, which seem to be made by copy-and-paste programming, were identified. Also, four major functionalities were identified in the MDS space. TARGET OSS PROJECTArgoUML (written in Java) MINING AREA-Change impact, propagation coupling analysis -Architecture and design quality analysis MINING QUESTIONSThe following two questions are addressed in this paper.(1) Which are similar class files?This question needs to be answered when one wants to refactor a Java program. Similar class files are often refactored into one class file to improve software maintainability. Also, when one modifies a class file, he/she often needs to find similar class files that need to be modified as well.(2) What are major functionalities among class files?This question needs to be answered when one joins a project and tries to understand the mapping between class files and their functionalities. INPUT DATAFrom 1,432 class files of ArgoUML release 0.20, 61 classes having more than 30 lines of source code were chosen as an input dataset.
This paper evaluates and discusses how different GPU programming frameworks affect the performance obtained from GPU acceleration of the striped smith-waterman algorithm used for biological sequence alignment. A total of 6 GPU implementations of the algorithm on NVIDIA GT200b and AMD RV870 using the CUDA and the OpenCL frameworks are compared to analyze cons and pros of explicit descriptions for architecture specific hardware mechanisms in the code. The evaluation results show that the primitive descriptions with the CUDA are still efficient especially for small size data, while better instruction scheduling and optimizations are carried out by the OpenCL compiler. On the other hand, the combination of OpenCL and RV870 which provides a relatively simple view of the architecture is efficient for the large data size.
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