Background: Selecting a suitable feature reduction technique. when building a defect prediction model, can be challenging. Different techniques can result in the selection of different independent variables which have an impact on the overall performance of the prediction model. To help in the selection, previous studies have assessed the impact of each feature reduction technique using different datasets. However, there are many reduction techniques, and therefore some of the well-known techniques have not been assessed by those studies. Aim: The goal of the study is to select a high-accuracy reduction technique from several candidates without preliminary assessments. Method: We utilized bandit algorithm (BA) to help with the selection of best features reduction technique for a list of candidates. To select the best feature reduction technique, BA evaluates the prediction accuracy of the candidates, comparing testing results of different modules with their prediction results. By substituting the reduction technique for the prediction method, BA can then be used to select the best reduction technique. In the experiment, we evaluated the performance of BA to select suitable reduction technique. We performed cross version defect prediction using 14 datasets. As feature reduction techniques, we used two assessed and two non-assessed techniques. Results: Using BA, the prediction accuracy was higher or equivalent than existing approaches on average, compared with techniques selected based on an assessment. Conclusions: BA can have larger impact on improving prediction models by helping not only on selecting suitable models, but also in selecting suitable feature reduction techniques.

Today's software has a very complex structure with multiple components, making it difficult to identify the cause of a fault. The process of identifying the cause of a fault may include referring to the logs from the system if they exist. But large and complex systems may generate a huge amount of logs, making the task of finding the important log messages to be a tedious task. In the case of systems that require continuous operation, the cause of faults must be identified quickly in an efficient manner. In this paper, we propose a method that identifies the log messages that are key for finding faults in server-side applications that has a tiered structure, such as LAMP (Linux, Apache, MySQL, PHP), and outputs logs (including traces during operation). The key part of our proposed approach is the application of Spectrum-Based Fault Localization (SBFL) to log files.

The Internet of Things (IoT) has been applied to home/office, healthcare, intelligent transportation and agriculture systems. The new IoT technology are growing rapidly and will play an essential role in our future societal lifestyle, economy and business. Currently, power hungry and radio wave interference are two big challenges hindering the IoT development. In this study, we propose a Markov localization algorithm to estimate positions of IoT devices considering various gateway allocation scenarios in a widespread and boundaryless field. We adopt the model-checking technique to validate the convergence of positions of the IoT devices. Our approach can accurately identify positions of IoT devices and connect each IoT node to its nearest gateways for sending sensing data and receiving commands from the cloud computing resources. We use a cattle-breeding IoT network as a case study to validate the proposed approach,which reduces IoT power consumption while enhancing the connectivity of the network. Additionally, we also discuss how to apply this simple approach to real-world IoT networks such as smart home and vehicular ad-hoc networks.

In this paper, we present a replicated study to predict fault-prone modules with code clone metrics to follow Baba's experiment. We empirically evaluated the performance of fault prediction models with clone metrics using 3 datasets from the Eclipse project and compared it to fault prediction without clone metrics. Contrary to the original Baba's experiment, we could not significantly support the effect of clone metrics, i.e., the result showed that F1-measure of fault prediction was not improved by adding clone metrics to the prediction model. To explain this result, this paper analyzed the relationship between clone metrics and fault density. The result suggested that clone metrics were effective in fault prediction for large modules but not for small modules.

Code clones (duplicated source code in a software system) are one of the major factors in decreasing maintainability. Many code clone detection methods have been proposed to find code clones automatically from large-scale software. However, it is still hard to find harmful code clones to improve maintainability because there are many code clones that should remain. Thus, to help find harmful code clones, we propose a code clone visualization method and a metrics application on the visualized information. Our method enables the location of harmful code clones diffused in a software system. We apply our method to three open source software programs and visualize their code clone information.