Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption

Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications

LineKing : coffee shop wait-time monitoring using smartphones

This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes

Eywa: Crowdsourced and cloudsourced omniscience

Abstract—Here we present our ubiquitous computing vision, Eywa. Eywa is an open publish-subscribe system that employs crowdsourcing for tasking and social networks & machine learning for identifying relevance. We argue that crowdsourcing (and the social networks and machine learning that enable it) should be a first class citizen in ubiquitous computing. We also observe that cloud computing is a natural platform to host such future ubiquitous computing systems. We discuss about some applications enabled by Eywa, and focus on our CuratedLiving application (which emphasizes “less choice more relevance ” approach) as a case study. Index Terms—Crowdsourced collaboration; Smartphones; Internet of things; Social networks; Cloud computin

Karamanlı Tartan ailesi ve Tartan Konağı hikayesi

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2016.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Öztürk, İbrahim Mert

Push and Pull Factors of Why Medical Students Want to Leave Türkiye: A Countrywide Multicenter Study

Phenomenon: Physician immigration from other countries is increasing as developed countries continue to be desirable destinations for physicians; however, the determinants of Turkish physicians’ migration decisions are still unclear. Despite its wide coverage in the media and among physicians in Türkiye, and being the subject of much debate, there is insufficient data to justify this attention. With this study, we aimed to investigate the tendency of senior medical students in Türkiye to pursue their professional careers abroad and its related factors. Approach: This cross-sectional study involved 9881 senior medical students from 39 different medical schools in Türkiye in 2022. Besides participants’ migration decision, we evaluated the push and pull factors related to working, social environment and lifestyle in Türkiye and abroad, medical school education inadequacy, and personal insufficiencies, as well as the socioeconomic variables that may affect the decision to migrate abroad. The analyses were carried out with a participation rate of at least 50%. Findings: Of the medical students, 70.7% had emigration intentions. Approximately 60% of those want to stay abroad permanently, and 61.5% of them took initiatives such as learning a foreign language abroad (54.5%) and taking relevant exams (18.9%). Those who wanted to work in the field of Research & Development were 1.37 (95% CI: 1.22–1.54) times more likely to emigrate. The push factor that was related to emigration intention was the “working conditions in the country” (OR: 1.89, 95% CI: 1.56–2.28) whereas the “social environment/lifestyle abroad” was the mere pull factor for the tendency of emigration (OR: 1.73, 95% CI: 1.45–2.06). In addition, the quality problem in medical schools also had a significant impact on students’ decisions (OR: 2.20, 95% CI: 1.83–2.65). Insights: Although the percentage of those who want to emigrate “definitely” was at the same level as in the other developing countries, the tendency to migrate “permanently” was higher in Türkiye. Improving working conditions in the country and increasing the quality of medical faculties seem vital in preventing the migration of physicians