Evaluating the applicability of reliability prediction models between different software

Abstract The prediction of fault-prone modules in a large software system is an important part in software evolution. Since prediction models in past studies have been constructed and used for individual systems, it has not been practically investigated whether a prediction model based on one system can also predict fault-prone modules accurately in other systems. Our expectation is that if we could build a model applicable to different systems, it would be extremely useful for software companies because they do not need to invest manpower and time for gathering data to construct a new model for every system. In this study, we evaluated the applicability of prediction models between two software systems through two experiments. In the first experiment, a prediction model using 19 module metrics as predictor variables was constructed in each system and was applied to the opposite system mutually. The result showed predictors were too fit to the base data and could not accurately predict fault-prone modules in the different system. On the basis of this result, we focused on a set of predictors showing great effectiveness in every model; and, in consequent, we identified two metrics (Lines of Code and Maximum Nesting Level) as commonly effective predictors in all the models. In the second experiment, by constructing prediction models using only these two metrics, prediction performance were dramatically improved. This result suggests that the commonly effective model applicable to more than two systems can be constructed by focusing on commonly effective predictors

Kinetic Analysis and Prediction of Thermal Decomposition Behavior of Tertiary Pyridine Resin in the Nitrate Form

AbstractThe thermal decomposition behavior of the tertiary pyridine resin, which was used during the nuclide-separation process in the Advanced Optimization by Recycling Instructive Elements (Advanced ORIENT) cycle, was investigated in its nitrate form (TPR-NO3), in order to determine ways of preventing its runaway reaction. A thermal analysis of TPR-NO3 and an analysis of the gases produced during decomposition were employed for the purpose. In addition, the kinetics parameters were evaluated via a kinetic analysis of the empirical thermal data. Finally, the validity of the reaction model was assessed by comparing the thermal behavior predicted by the estimated reaction model with that determined by the results of a gram-scale heating test performed in our previous study. We found that, when TPR-NO3 was heated, first, nitric acid was removed. Subsequently, TPR-NO3 was oxidized by the removed nitric acid. Under the assumption that it took place an autocatalytic oxidation and nth order thermal decomposition in parallel, the thermogravimetric analysis data could be fitted very well using a nonlinear regression model. The thermal behavior of TPR-NO3 could be predicted by the reaction model determined in this study under conditions where the cooling effect owing to evaporation was ignored. In addition, the maximum temperature and time to maximum rate of a runaway reaction predicted using the determined reaction model gave the result on the side of prudence

Preference Analysis Method Applying Relationship between Electroencephalogram Activities and Egogram in Prefrontal Cortex Activities : How to collaborate between engineering techniques and psychology

This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a small number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: θ, low-β, and high-β. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before the improvement of the input vector