Mining Fix Patterns for FindBugs Violations

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin

Social networking and open educational resources: updating quality assurance for e-learning excellence

Quality assurance approaches in higher education are well-established, but it is important to develop methods which are applicable to the domain of e-learning. The E-xcellence methodology (EADTU, 2009a) was therefore designed to assess the quality of e-learning in distance learning and blended learning contexts. The methodology is based around a set of benchmarks, supported by a practitioner handbook and a web-based ‘QuickScan’ self-evaluation tool. Experience shows that the E-xcellence methodology is particularly valuable for the process of improvement through collaborative internal review. E-learning has evolved since the E-xcellence methodology was first developed. In particular, there is increasing awareness and use of open education resources (OERs) and social networking. However, these aspects were not explicit in the original E-xcellence resources. The E-xcellence Next project was therefore established to update the resources, incorporating these developments. To begin this process, a consultation was carried out among E-xcellence Next project members, followed by a participatory workshop on the themes of social networking and OERs. The E-xcellence resources were also used in a series of self-evaluation seminars held at European higher education institutions. Experience and feedback from these activities has been used to update the manual, the benchmarks and the QuickScan tool. The result is a set of quality assurance resources which encompass social networking, OERs and other recent developments in e-learning

On Productivity Measurement and Interpretation: Some Insights on Italy in the European Context. LEQS Paper No. 142/2019 March 2019

Over the period 1995–2016, the Italian performance in terms of productivity was poor in historical terms and in comparison with its main international partners. This issue goes beyond Italy, with declining productivity growth observed, from the second half of the nineties, in several other advanced economies. Possible explanations for the slowdown include factors such as lower capital investment by firms, decreased competition, excessive regulation, and capital misallocation. The diffuse slowing rates of measured productivity growth has also raised questions on whether GDP and output current compilation methods are adequate (i.e. the mis-measurement hypothesis). The “ICT revolution” has created new ways of exchanging and providing goods and services as result of increased connectivity. These developments challenge the way economic activity is traditionally measured. There are also measurement problems associated with estimating output and input volumes especially related to the quality of price indexes for some products and services. These problems have an impact on productivity estimates and might impair international comparability. In this paper, we intend to investigate what the core problems in productivity measurement and interpretation are in the European context, with a specific focus on Italy

SZZ Unleashed: An Open Implementation of the SZZ Algorithm -- Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project

Numerous empirical software engineering studies rely on detailed information about bugs. While issue trackers often contain information about when bugs were fixed, details about when they were introduced to the system are often absent. As a remedy, researchers often rely on the SZZ algorithm as a heuristic approach to identify bug-introducing software changes. Unfortunately, as reported in a recent systematic literature review, few researchers have made their SZZ implementations publicly available. Consequently, there is a risk that research effort is wasted as new projects based on SZZ output need to initially reimplement the approach. Furthermore, there is a risk that newly developed (closed source) SZZ implementations have not been properly tested, thus conducting research based on their output might introduce threats to validity. We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories. This paper describes our implementation along with a usage example for the Jenkins project, and conclude with an illustrative study on just-in-time bug prediction. We hope to continue evolving SZZ Unleashed on GitHub, and warmly invite the community to contribute