Identifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and uses data mining approaches to derive the aggregation metrics of traffic intensity data from the city of Madrid. This work uses a novel similarity measure by utilizing the results of the Wilcoxon Signed Rank test across 2018 locations to discover similarities. We propose a Genetic Algorithm on the results of the Wilcoxon test for forming communities based on the aggregation metrics. This work also compares and evaluates the performance of the proposed algorithm against standard distance measures and other state-of-the-art approaches. For finding the optimal number of possible communities in the data, we have taken the help of Davies -Bouldin Test. Our experimental results show the effectiveness of the Genetic Algorithm using various parameters, such as number of dissimilar points within a cluster, minimum number of dissimilar data points between clusters and overall based on Modified Silhouette coefficient. Furthermore, we find that our method is able to distribute the data points in a more uniform manner across formed communities in comparison to other approaches considered in this work.
Reliability of any software plays a very important role to assess the quality of the software systems. Reliability analysis and evaluation during early phases of software development life cycle (SDLC) significantly help developer and analysts in the proper allocation of limited resources during testing and maintenance process. The goal of this work is to develop one model using the measurement of the internal structure of the software system i.e., source code metrics for predicting the reason of failure in software during continuously running for a certain time i.e., software aging. Aging-Related Bugs are related with failure during the execution of the software, that leads to degradation in quality, system crashing, misuse of resources, etc.. In this paper, seven different sets of software metrics, seven model training algorithms, one data sampling technique have been empirically investigated and evaluated for predicting aging-related bugs classes. The trained aging-related bugs prediction models are validated using 5-fold cross-validation techniques. The final observation of this experiment work is assessed over seven open source application software systems. The high-value of performance parameters confirm the predicting capability of data sampling, sets of metrics, and training algorithms to predict aging-related bugs classes.
Reliability of any software plays a very important role to assess the quality of the software systems. Reliability analysis and evaluation during early phases of software development life cycle (SDLC) significantly help developer and analysts in the proper allocation of limited resources during testing and maintenance process. The goal of this work is to develop one model using the measurement of the internal structure of the software system i.e., source code metrics for predicting the reason of failure in software during continuously running for a certain time i.e., software aging. Aging-Related Bugs are related with failure during the execution of the software, that leads to degradation in quality, system crashing, misuse of resources, etc.. In this paper, seven different sets of software metrics, seven model training algorithms, one data sampling technique have been empirically investigated and evaluated for predicting agingrelated bugs classes. The trained aging-related bugs prediction models are validated using 5-fold cross-validation techniques. The final observation of this experiment work is assessed over seven open source application software systems. The high-value of performance parameters confirm the predicting capability of data sampling, sets of metrics, and training algorithms to predict aging-related bugs classes.
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