2010

• Emanuel Giger, Martin Pinzger, Harald C. Gall, Predicting the fix time of bugs, Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering, May 2010. (inproceedings)
  

  Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.

  
• Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, Bart Goethals, Predicting the severity of a reported bug, Proceedings of the 7th Working Conference on Mining Software Repositories 2010. (inproceedings)
  

  The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65-0.75 with Mozilla and Eclipse; 0.70-0.85 in the case of GNOME).

  

2009

• Beat Fluri, Michael Würsch, Emanuel Giger, Harald C. Gall, Analyzing the co-evolution of comments and source code, Software Quality Journal Vol. 17 (4), September 2009. (article)
  

  Source code comments are a valuable instrument to preserve design decisions and to communicate the intent of the code to programmers and maintainers. Nevertheless, commenting source code and keeping them up-to-date is often neglected for reasons of time or programmer?s obliviousness. In this paper, we investigate the question whether developers comment their code and to which extent they add comments or adapt them when they evolve the code. We present an approach to associate comments with source code entities to track their co-evolution over multiple versions. A set of heuristics are used to decide whether a comment is associated to its preceding or its succeeding source code entity. We analyzed the co-evolution of code and comments in eight different open source and closed source software systems. We found with statistical significance that (1) the relative amount of comments and source code grows at about the same rate; (2) the type of a source code entity, such as a method declaration or an if-statement, has a significant influence on whether or not it gets commented; (3) in six out of the eight systems, code and comments co-evolve in 90 percent of the cases; and (4) surprisingly, API changes and comments do not co-evolve but they are re-documented in a later revision. As a result, our approach enables a quantitative assessment of the commenting process in a software system. We can, therefore, leverage the results to provide feedback during development to increase the awareness when to add comments or when to adapt comments because of source code changes.

  
• Thomas Zimmermann, Nachiappan Nagappan, Harald C. Gall, Emanuel Giger, Brendan Murphy, Cross-project defect prediction: a large scale experiment on data vs. domain vs. process, ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering on European software engineering conference and foundations of software engineering 2009, ACM. (inproceedings)
  
  

2008

• Yu Zhou, Michael Würsch, Emanuel Giger, Harald C. Gall, Jian Lue, A Bayesian Network Based Approach for Change Coupling Prediction, WCRE '08: Proceedings of the 2008 15th Working Conference on Reverse Engineering 2008, IEEE Computer Society. (inproceedings)
  

  Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research, in this paper, we inspect different dimensions of software changes including change significance or source code dependency levels, extract a set of features from the two sources and propose a bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work.

  
• Beat Fluri, Emanuel Giger, Harald C. Gall, Discovering Patterns of Change Types, Proceedings of the 23rd International Conference on Automated Software Engineering, September 2008, IEEE Computer Society. (inproceedings)
  

  The reasons why software is changed are manyfold; new features are added, bugs have to be fixed, or the consistency of coding rules has to be re-established. Since there are many types of of source code changes we want to explore whether they appear frequently together in time and whether they describe specific development activities. We describe a semi-automated approach to discover patterns of such change types using agglomerative hierarchical clustering. We extracted source code changes of one commercial and two open-source software systems and applied the clustering. We found that change type patterns do describe development activities and affect the control flow, the exception flow, or change the API.

  

2006

• Emanuel Giger, Evolving Code Clones An Approach towards a Fine-Grained Analysis of Code Clone Changes and Change Couplings 2006. (diplomathesis)
  

  The term code clone in the field of software engineering refers to the fact that a software system contains duplications in its source code. Such code clones are marked as bad smell. They are assumed to cause problems during the evolution and maintenance of a system, because programmers and developers may need to locate code clones in the entire source code to change them consistently. This problem manifests itself in change coupling groups ? group of source code files that are often changed together. It is thus of importance to have a methodology to identify and ?disarm? such critical files specifically. A systematical correlation between code clones and change couplings has been assumed so far. Recent research activities could neither verify this correlation nor totally reject it. In this thesis we use a new approach that combines various technologies to investigate the relation between code clones and change couplings. We applied our approach on two case studies. The evaluation of the results could not establish a systematic correlation or an interaction between code clones and change couplings. Nevertheless the case studies pointed out certain file groups in which code clones indeed caused change couplings. The approach developed in this thesis can be used to investigate a software system, and to identify such critical file groups in a well defined process.

  Der Begriff Klon bezieht sich im Gebiet der Software Entwicklung auf die Tatsache, dass ein Software System Duplizierungen in seinem Quelltext aufweist. Klone haben den Ruf eines bad smell. Man nimmt daher an, dass sie Probleme waehrend der Evolution und Wartung eines Software Systems bereiten. Dies ruehrt daher, dass Klone oft konsistent an allen Stellen, an welchen sie im gesamten Quelltext auftreten, angepasst werden muessen. Dieses Problem aeussert sich in einer Gruppe von Dateien, die oft zusammen geaendert werden. Umso wichtiger waere eine Methodik, die es erlaubt, kritische Dateien und Klone gezielt zu identifizieren und zu ?entschaerfen?. Es gilt seit laengerem die Vermutung, dass ein Zusammenhang zwischen solchen Quelltext-Klonen und dem Aenderungsverhalten der betroffenen Dateien besteht. Diese Vermutung konnte in frueheren Untersuchungen nicht bestaetigt, jedoch auch nicht gaenzlich verneint werden. In dieser Diplomarbeit wird eine neue Kombination von Techniken verwendet, um die Zusammenhaenge und Wechselwirkungen zwischen Duplizierungen und Aenderungskopplungen zu untersuchen. In unseren Fallstudien konnten wir keinen solchen systematischen Zusammenhang feststellen. Dateien mit einer hoeheren Anzahl von Klonen, werden nicht zwingend oefter miteinander geaendert. Die Untersuchungen haben jedoch gezeigt, dass in gewissen Faellen gemeinsame Aenderungen tatsaechlich auf Klone zurueck zu fuehren sind. Unser Ansatz kann verwendet werden, um diese Faelle gezielt in einem System zu suchen, und im Zuge eines Refactoring zu entfernen.

  

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