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Code style reflects the choice of textual representation of source code. This study, for the first time, explores whether code style can be used to identify good programmers with a vision that recruitment process in the software industry can be improved. For analysis, solutions from Google Code Jam were selected. The study used cluster analysis to find association between good programmers and style clusters. Furthermore, supervised machine learning models were trained with stylistic features to predict good programmers. Results reveal that, although association between programmers with particular clusters could not be concluded, supervised learning models can predict good programmers.

While the sole purpose of undergraduate education is not to prepare the students for the industry, it is certainly one of its important objectives. In this work, we investigate how well the Bangladeshi software and IT-related undergraduate education prepares the students for the software industry. We conducted semi-structured interviews of twenty practitioners from the Bangladeshi software industry. During the interviews, these practitioners provided commentary on where they believe the undergraduate education system falls short, and provided their suggestions for improvement. Based on the themes discovered from the interviews, we created a survey where more than two hundred practitioners participated. The results of our work suggest that most of the practitioners believe that, while some aspects of the undergraduate education are fine, the undergraduate education system leaves its graduates largely unprepared for the software industry. In this paper, we summarize and present the practitioners' opinions on some key areas including but not limited to updating of syllabi, internships as part of the curricula, the nature, length and evaluation process of undergraduate projects, pedagogical issues, and academic practices.

Defect prediction is one of the most popular research topics due to its potentiality to minimize software quality assurance effort. Existing approaches have examined defect prediction from various perspective such as complexity and developer metrics. However, none of these consider programming style for defect prediction. This paper aims at analyzing the impact of stylistic metrics on both within-project and crossproject defect prediction. For prediction, 4 widely used machine learning algorithms namely Naive Bayes, Support Vector Machine, Decision Tree and Logistic Regression are used. The experiment is conducted on 14 releases of 5 popular, open source projects. F1, Precision and Recall are inspected to evaluate the results. Results reveal that stylistic metrics are good predictor of defects.