Software development is a largely collaborative effort, of which the actual encoding of program logic in source code is a relatively small part. Software developers have to collaborate effectively and communicate with their peers in order to avoid coordination problems. To date, little is known how developer communication during software development activities impacts the quality and evolution of a software. In the doctoral thesis presented to this symposium, we introduce and evaluate tools and techniques to recover communication data from traces of the software development activities that are recorded in software repositories. Using this data, we present a study on the impact of developer communication on the quality and evolution of the software through a tiered study. Among the highlights of the presented dissertation, we find that communication between developers stands in a direct relationship to the quality of the software. In addition, we find that communication between developers plays a important role in maintaining a healthy contribution management process, which is one of the key factors to the successful evolution of the software. Our dissertation illustrates that software development is an intricate and complex process, which is strongly influenced by the social interactions between the stakeholders involved in the development activities. A traditional view based solely on technical aspects of software development such as source code size and complexity, while valuable, limits our understanding of software development activities. The presented research consists of a first step towards gaining a more holistic view on software development activities.

Software teams record their work progress in task repositories which often require them to encode their activities in a set of edits to field values in a form-based user interface. When others read the tasks, they must decode the schema used to write the activities down. We interviewed four software teams and found out how they used the task repository fields to record their work activities. However, we also found that they had trouble interpreting task revisions that encoded for multiple activities at the same time. To assist engineers in decoding tasks, we developed a scalable method based on frequent pattern mining to identify patterns of frequently co-edited fields that each represent a conceptual work activity. We applied our method to our two years of our interviewee's task repositories and were able to abstract 83,000 field changes into just 27 patterns that cover 95% of the task revisions. We used the 27 patterns to render the teams' tasks in web-based English newsfeeds and evaluated them with the product teams. The team agreed with most of our patterns and English interpretations, but outlined a number of improvements that we will incorporate into future work.

Much research energy in software engineering is focused on the creation of effort and defect prediction models. Such models are important means for practitioners to judge their current project situation, optimize the allocation of their resources, and make informed future decisions. However, software engineering data contains a large amount of variability. Recent research demonstrates that such variability leads to poor fits of machine learning models to the underlying data, and suggests splitting datasets into more fine-grained subsets with similar properties. In this paper, we present a comparison of three different approaches for creating statistical regression models to model and predict software defects and development effort. Global models are trained on the whole dataset. In contrast, local models are trained on subsets of the dataset. Last, we build a global model that takes into account local characteristics of the data. We evaluate the performance of these three approaches in a case study on two defect and two effort datasets. We find that for both types of data, local models show a significantly increased fit to the data compared to global models. The substantial improvements in both relative and absolute prediction errors demonstrate that this increased goodness of fit is valuable in practice. Finally, our experiments suggest that trends obtained from global models are too general for practical recommendations. At the same time, local models provide a multitude of trends which are only valid for specific subsets of the data. Instead, we advocate the use of trends obtained from global models that take into account local characteristics, as they combine the best of both worlds.

Developer communication through email, chat, or issue report comments consists mostly of largely unstructured data, i.e., natural language text, mixed with technical artifacts such as project-specific jargon, abbreviations, source code patches, stack traces and identifiers. These technical artifacts represent a valuable source of knowledge on the technical part of the system, with a wide range of applications from establishing traceability links to creating project-specific vocabularies. However, the lack of well-defined boundaries between natural language and technical content make the automated mining of technical artifacts challenging. As a first step towards a general-purpose technique to extracting technical artifacts from unstructured data, we present a lightweight approach to untangle technical artifacts and natural language text. Our approach is based on existing spell checking tools, which are well-understood, fast, readily available across platforms and impartial to different kinds of textual data. Through a handcrafted benchmark, we demonstrate that our approach is able to successfully uncover a wide range of technical artifacts in unstructured data.

Software development is a largely collaborative effort, of which the actual encoding of program logic in source code is a relatively small part. Yet, little is known about the impact of collaboration between stakeholders on software quality. We hypothesize that the collaboration between stakeholders during software development has a non-negligible impact on the software system. Information about collaborative activities can be recovered from traces of their communication, which are recorded in the repositories used for the development of the software system. This thesis contributes the following: 1) to make this information accessible for practitioners and researchers, we present approaches to distill communication information from development repositories, and empirically validate our proposed extractors. 2) By linking back the extracted communication data to the parts of the software system under discussion, we are able to empirically study the impact of communication, as a proxy to collaboration between stakeholders, on a software system. Through case studies on a broad spectrum of open-source software projects, we demonstrate the important role of social interactions between stakeholders with respect to the evolution of a software system.

Correcting software defects accounts for a significant amount of resources such as time, money and personnel. To be able to focus testing efforts where needed the most, researchers have studied statistical models to predict in which parts of a software future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on file-oriented measures, such as source code and churn metrics. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the repositories used to manage development efforts. In this paper, we investigate statistical models, to study the impact of social structures between developers and end-users on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of a large open-source software project. The results of our case study are promising and indicate that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our findings suggest that social information does not substitute, but rather augments traditional product and process-based metrics used in defect prediction models.

Development mailing lists play a central role in facilitating communication in open source projects. Since these lists frequently host design and project discussions, knowledgeable contribution to these discussion threads is essential to avoid mis-communication that might slow-down the progress of a project. However, given the sheer volume of emails on these lists, it is easy to miss important discussions. To find out how developers are able to deal with mailing list discussions, we study the main factors that encourage developers to contribute to the development mailing lists. We develop personalized models to automatically identify discussion threads that a developer would contribute to based on his previous contribution behavior. Case studies on development mailing lists of three open source projects (Apache, PostgreSQL and Python) show that the average accuracy of our models is 89-85% and that the models vary significantly between different developers.

A widely shared belief in the software engineering community is that stack traces are much sought after by developers to support them in debugging. But limited empirical evidence is available to confirm the value of stack traces to developers. In this paper, we seek to provide such evidence by conducting an empirical study on the usage of stack traces by developers from the ECLIPSE project. Our results provide strong evidence to this effect and also throws light on some of the patterns in bug fixing using stack traces. We expect the findings of our study to further emphasize the importance of adding stack traces to bug reports and that in the future, software vendors will provide more support in their products to help general users make such information available when filing bug reports.

In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report. The analysis of the 466 responses revealed an information mismatch between what developers need and what users supply. Most developers consider steps to reproduce, stack traces, and test cases as helpful, which are, at the same time, most difficult to provide for users. Such insight is helpful for designing new bug tracking tools that guide users at collecting and providing more helpful information. Our CUEZILLA prototype is such a tool and measures the quality of new bug reports; it also recommends which elements should be added to improve the quality. We trained CUEZILLA on a sample of 289 bug reports, rated by developers as part of the survey. The participants of our survey also provided 175 comments on hurdles in reporting and resolving bugs. Based on these comments, we discuss several recommendations for better bug tracking systems, which should focus on engaging bug reporters, better tool support, and improved handling of bug duplicates.

Mailing list repositories contain valuable information about the history of a project. Research is starting to mine this information to support developers and maintainers of long-lived software projects. However, such information exists as unstructured data that needs special processing before it can be studied. In this paper, we identify several challenges that arise when using off-the-shelf techniques for processing mailing list data. Our study highlights the importance of proper processing of mailing list data to ensure accurate research results.

In a survey we found that most developers have experienced duplicated bug reports, however, only few considered them as a serious problem. This contradicts popular wisdom that considers bug duplicates as a serious problem for open source projects. In the survey, developers also pointed out that the additional information provided by duplicates helps to resolve bugs quicker. In this paper, we therefore propose to merge bug duplicates, rather than treating them separately. We quantify the amount of information that is added for developers and show that automatic triaging can be improved as well. In addition, we discuss the different reasons why users submit duplicate bug reports in the first place.

In software engineering experiments, the description of bug reports is typically treated as natural language text, although it often contains stack traces, source code, and patches. Neglecting such structural elements is a loss of valuable information; structure usually leads to a better performance of machine learning approaches. In this paper, we present a tool called infoZilla that detects structural elements from bug reports with near perfect accuracy and allows us to extract them. We anticipate that infoZilla can be used to leverage data from bug reports at a different granularity level that can facilitate interesting research in the future.

In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report.