Birgit Hofer

Thomas Hirsch, Birgit Hofer

Bug reports are a popular target for natural language processing (NLP). However, bug reports often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the bug reports with noise, but often constitute a real problem for the NLP approach at hand and have to be removed. In this paper, we present a machine learning based approach to classify content into natural language and artifacts at line level implemented in Python. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for bug reports. Our model scores at 0.95 ROC-AUC and 0.93 F1 against our manually annotated validation set, and classifies 10k lines in 0.72 seconds. We cross evaluated our model against a foreign dataset and a foreign R model for the same task. The Python implementation of our model and our datasets are made publicly available under an open source license.

Thomas Hirsch, Birgit Hofer

Researchers have developed numerous debugging approaches to help programmers in the debugging process, but these approaches are rarely used in practice. In this paper, we investigate how programmers debug their code and what researchers should consider when developing debugging approaches. We conducted an online questionnaire where 102 programmers provided information about recently fixed bugs. We found that the majority of bugs (69.6 %) are semantic bugs. Memory and concurrency bugs do not occur as frequently (6.9 % and 8.8 %), but they consume more debugging time. Locating a bug is more difficult than reproducing and fixing it. Programmers often use only IDE build-in tools for debugging. Furthermore, programmers frequently use a replication-observation-deduction pattern when debugging. These results suggest that debugging support is particularly valuable for memory and concurrency bugs. Furthermore, researchers should focus on the fault localization phase and integrate their tools into commonly used IDEs.

Birgit Hofer, Jorge Mendes

Proceedings of the 5th International Workshop on Software Engineering Methods in Spreadsheets (SEMS'18), held on October 1st, 2018, in Lisbon, Portugal, and co-located with the 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

Patrick Koch, Konstantin Schekotihin, Dietmar Jannach et al.

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.