With the steady advances in tooling to support software engineering, mastering all the features of modern IDEs, version control systems, and project trackers is becoming increasingly difficult. Answering even the most common developer questions can be surprisingly tedious and difficult. In this article we present a user study with 35 subjects to evaluate our quasi-natural language interface that provides access to various facets of the evolution of a software system but requires almost zero learning effort. Our approach is tightly woven into the Eclipse IDE and allows developers to answer questions related to source code, development history, or bug and issue management. The results of our evaluation show that our query interface can outperform classical software engineering tools in terms of correctness, while yielding significant time savings to its users and greatly advancing the state of the art in terms of usability and learnability.

The concept of Software as a Service has been recently introduced, allowing software to be accessed, composed into workflows, and executed over the Internet. However, there is no guarantee on the execution reliability of such workflows. In case of component service failure, that particular service has to be replaced quickly to ensure the correct execution of the composite service. While most of the existing replacement approaches require the faulty workflow to be completely re-executed, we advocate that dependencies among component services is vitally important for service replacement. In this paper, we present a dependency-based component service recommendation approach that takes into account the dependencies among the component services, such as input/output, pre and post conditions, and correlation among services. Correlation analysis is a new dimension that we introduce to the service replacement algorithms. To demonstrate our approach, we use an existing software analysis services framework as a case study. Finally, we argue that dependencies among services can affect the accuracy of the replacement.

Researchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. However, most of those approaches predict bugs on file-level. This often leaves developers with a considerable amount of effort to examine all methods of a file until a bug is located. This particular problem is reinforced by the fact that large files are typically predicted as the most bug-prone. In this paper, we present bug prediction models at the level of individual methods rather than at file-level. This increases the granularity of the prediction and thus reduces manual inspection efforts for developers. The models are based on change metrics and source code metrics that are typically used in bug prediction. Our experiments---performed on 21 Java open-source (sub-)systems---show that our prediction models reach a precision and recall of 84% and 88%, respectively. Furthermore, the results indicate that change metrics significantly outperform source code metrics.

There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.

A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65-0.75 with Mozilla and Eclipse; 0.70-0.85 in the case of GNOME).

Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies focused on transferring prediction models from one project to another. In this paper, we study cross-project defect prediction models on a large scale. For 12 real-world applications, we ran 622 cross-project predictions. Our results indicate that cross-project prediction is a serious challenge, i.e., simply using models from projects in the same domain or with the same process does not lead to accurate predictions. To help software engineers choose models wisely, we identified factors that do influence the success of cross-project predictions. We also derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.

The reasons why software is changed are manyfold; new features are added, bugs have to be fixed, or the consistency of coding rules has to be re-established. Since there are many types of of source code changes we want to explore whether they appear frequently together in time and whether they describe specific development activities. We describe a semi-automated approach to discover patterns of such change types using agglomerative hierarchical clustering. We extracted source code changes of one commercial and two open-source software systems and applied the clustering. We found that change type patterns do describe development activities and affect the control flow, the exception flow, or change the API.