Preventive maintenance

The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.

Objective : This research presents a new method for scheduling troubleshooting operations of station regulators in natural gas distribution stations, focusing on the importance index of equipment, reliability, and risk management. Methods : Using reliability-based maintenance principles and the expertise of professionals from Isfahan Gas Company, we selected 166 regulators from 112 pressure reduction stations in Isfahan. We assessed the importance index of each station and evaluated the potential consequences of its failure risks, followed by calculating its reliability metrics. The results were grouped using the K-means clustering method. Ultimately, we identified the optimal time frame for conducting troubleshooting operations.  Results : In this study, 166 regulators were grouped into three clusters. The average time required to perform troubleshooting activities varied among the clusters. For the first cluster, the average time was determined to be 48 hours. The second cluster had an average troubleshooting time of 544 hours, while the third cluster had an average of 829 hours. Currently, the average time for troubleshooting regulators is 720 hours. Conclusion : This paper presents the following contributions: 1. Identification of the station importance index based on the gas supply mission to subscribers and end consumers. 2. Localization of the method for estimating risks and consequences arising from station equipment failures. 3. Assessment of equipment reliability. 4. Clustering of key regulatory equipment in the case study.