Background. Today, redundancy in source code, so-called ''clones'' caused by copy &paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

No abstract

Communication is essential in software engineering. Especially in distributed open-source teams, communication needs to be supported by channels including mailing lists, forums, issue trackers, and chat systems. Yet, we do not have a clear understanding of which communication channels stakeholders in open-source projects use. In this study, we fill the knowledge gap by investigating a statistically representative sample of 400 GitHub projects. We discover the used communication channels by regular expressions on project data. We show that (1) half of the GitHub projects use observable communication channels; (2) GitHub Issues, e-mail addresses, and the modern chat system Gitter are the most common channels; (3) mailing lists are only in place five and have a lower market share than all modern chat systems combined.

Group student software projects are important in computer science education. Students are encouraged to self-organize and learn technical skills, preparing them for real life software development. However, the projects contribute to multiple learning objectives, making coaching students a time consuming task. Thus, it is important to have a suitable best practice development process. For providing better insights for the students, the resulting software has to be of value and meet quality requirements, including maintainability, as in real life software development. Using source code quality metrics and by data mining repository data like commit history, we analyze six student projects, measuring their quality and identifying contributing factors to success or failure of a student project. Based on the findings, we formulate recommendations to improve future projects for students and researchers alike.

No abstract

Abstract. Communication in teams is an important but difficult issue. In a Scrum development process, we use the Daily Scrum meetings to inform others about important problems, news and events in the project. When persons are absent due to holiday, illness or travel, they miss relevant information because there is no document that protocols the content of these meetings. We present a concept and a Twitter-like tool that improves communication in a Scrum development process. We take advantage out of the observation that many people do not like to create documentation but they do like to share what they did. We used the tool in industrial practice and observed an improvement in communication.

Background. Today, redundancy in source code, so-called "clones", caused by copy&paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, caused not by copy&-paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research.Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs.Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in < 16 % of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories.Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

No abstract