NIER session

Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of ``temporal redundancy''. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.

Bug reports seldom contain information about the steps to reproduce a failure. Therefore, failure reproduction is a time consuming, difficult, and sometimes impossible task for software developers. Users are either unaware of the importance of steps to reproduce, are unable to describe them, or do not have time to report them. Similarly, automated crash reporting tools usually do not capture this information. In order to tackle this problem, we propose to exploit the action history of undo features, i.e. the history of user actions captured by many applications in order to allow users to undo previous actions. As it is captured anyway, our approach does not introduce additional monitoring overhead. We propose to extract the action history upon occurrence of a failure and present it to developers during bug fixing. Our hypothesis is that information about user actions contained in the action history of undo features enables developers to reproduce failures. We support this hypothesis with anecdotal evidence from a small empirical study of bug reports. A thorough evaluation is necessary to investigate the applicability and impact of our approach and to compare it to existing capture/ replay approaches.

Big data became problems not just how to analyze and visualize but also how to interact with the data. In software analysis and maintenance, bug tracking system receive feedbacks of the software project users everyday, which means that the data is increasing everyday. A large-scale bug tracking system that contains large amount of information does not give end users an easy way to analyze bug information because it lacks of good interaction system. We present Bugarium that integrate 3D Motion Controller and data-driven documents to ease both interaction and visualization on a large-scale bug repository. Bugarium leads to a significant increase in terms of using 3D motion controller to operate big data in software visualization. An user study shows that Bugarium made users satisfied while using it to interact and visualize a large-scale bug tracking system.

Regular expressions are a widely used programming technique, but seem to be neglected by software engineering research. Encoding complex string parsing in a very compact notation, their complexity and compactness, however, introduce particular challenges with respect to program comprehension. In this paper, we present RegViz, an approach to visually augment regular expressions without changing their original textual notation. The visual encoding clarifies the structure of the regular expressions and clearly discerns included tokens by function. The approach also provides advanced visual highlighting of matches in a sample text and defining test cases therein. We implemented RegViz as a Web-based tool for JavaScript regular expressions. Expert feedback suggests that the approach is intuitive to apply and increases the readability of regular expressions.

We present an empirical analysis of defect arrival data in the operational phase of multiple software products. We find that the shape of the defect curves is sufficiently determined by three external and readily available release cycle attributes: the product type, the license model, and the cycle time between releases. This finding provides new insights into the driving forces affecting the specifics of defect curves and opens up new opportunities for software support organizations to reduce the cost of maintaining defect arrival models for individual products. In addition, it allows the possibility of predicting the defect arrival rate of one product from another with similar known attributes.

Bug classification is a well-established practice which supports important activities such as enhancing verification and validation (V&V) efficiency and effectiveness. The state of the practice is manual and hence classification errors occur. This paper investigates the sensitivity of the value of bug classification (specifically, failure type classification) to its error rate; i.e., the degree to which misclassified historic bugs decrease the V&V effectiveness (i.e., the ability to find bugs of a failure type of interest). Results from the analysis of an industrial database of more than 3,000 bugs show that the impact of classification error rate on V&V effectiveness significantly varies with failure type. Specifically, there are failure types for which a 5% classification error can decrease the ability to find them by 66%. Conversely, there are failure types for which the V&V effectiveness is robust to very high error rates. These results show the utility of future research aimed at: 1) providing better tool support for decreasing human errors in classifying the failure type of bugs, 2) providing more robust approaches for the selection of V&V techniques, and 3) including robustness as an important criterion when evaluating technologies.