Code review is an essential element of any mature software development project; it aims at evaluating code contributions submitted by developers. In principle, code review should improve the quality of code changes (patches) before they are committed to the project's master repository. In practice, bugs are sometimes unwittingly introduced during this process.In this paper, we report on an empirical study investigating code review quality for Mozilla, a large open-source project. We explore the relationships between the reviewers' code inspections and a set of factors, both personal and social in nature, that might affect the quality of such inspections. We applied the SZZ algorithm to detect bug-inducing changes that were then linked to the code review information extracted from the issue tracking system. We found that 54% of the reviewed changes introduced bugs in the code. Our findings also showed that both personal metrics, such as reviewer workload and experience, and participation metrics, such as the number of involved developers, are associated with the quality of the code review process.

-The paper presents an exploratory study of two feature location techniques utilizing three strategies for splitting identifiers: CamelCase, Samurai and manual splitting of identifiers. The main research question that we ask in this study is if we had a perfect technique for splitting identifiers, would it still help improve accuracy of feature location techniques applied in different scenarios and settings? In order to answer this research question we investigate two feature location techniques, one based on Information Retrieval and the other one based on the combination of Information Retrieval and dynamic analysis, for locating bugs and features using various configurations of preprocessing strategies on two open-source systems, Rhino and jEdit. The results of an extensive empirical evaluation reveal that feature location techniques using Information Retrieval can benefit from better preprocessing algorithms in some cases, and that their improvement in effectiveness while using manual splitting over state-of-the-art approaches is statistically significant in those cases. However, the results for feature location technique using the combination of Information Retrieval and dynamic analysis do not show any improvement while using manual splitting, indicating that any preprocessing technique will suffice if execution data is available. Overall, our findings outline potential benefits of putting additional research efforts into defining more sophisticated source code preprocessing techniques as they can still be useful in situations where execution information cannot be easily collected.

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SUMMARYThe software engineering literature reports empirical evidence on the relation between various characteristics of a software system and its quality. Among other factors, recent studies have shown that a proper choice of identifiers influences understandability and maintainability. Indeed, identifiers are developers' main source of information and guide their cognitive processes during program comprehension when high-level documentation is scarce or outdated and when source code is not sufficiently commented. This paper proposes a novel approach to recognize words composing source code identifiers. The approach is based on an adaptation of Dynamic Time Warping used to recognize words in continuous speech. The approach overcomes the limitations of existing identifier-splitting approaches when naming conventions (e.g., Camel Case) are not used or when identifiers contain abbreviations. We apply the approach on a sample of more than 1000 identifiers extracted from 340 C programs and compare its results with a simple Camel Case splitter and with an implementation of an alternative identifier splitting approach, Samurai. Results indicate the capability of the novel approach: (i) to outperform the alternative ones, when using a dictionary augmented with domain knowledge or a contextual dictionary and (ii) to expand 48% of a set of selected abbreviations into dictionary words.

Abstract-The existing software engineering literature has empirically shown that a proper choice of identifiers influences software understandability and maintainability. Researchers have noticed that identifiers are one of the most important source of information about program entities and that the semantic of identifiers guide the cognitive process.Recognizing the words forming identifiers is not an easy task when naming conventions (e.g., Camel Case) are not used or strictly followed and-or when these words have been abbreviated or otherwise transformed. This paper proposes a technique inspired from speech recognition, i.e., dynamic time warping, to split identifiers into component words.The proposed technique has been applied to identifiers extracted from two different applications: JHotDraw and Lynx. Results compared to manually-built oracles and with Camel Case algorithm are encouraging. In fact, they show that the technique successfully recognizes words composing identifiers (even when abbreviated) in about 90% of cases and that it performs better than Camel Case. Furthermore, it was able to spot mistakes in the manually-built oracle.