Marcel Bruch

FrUiT: IDE support for framework understanding

Frameworks provide means to reuse existing design and functionality, but first require developers to understand how to use them. Learning the correct usage of a framework can be difficult due to the large number of rules to obey and the complex collaborations between the classes. We propose the use of data mining techniques to extract reuse patterns from existing framework instantiations. Based on these patterns, suggestions about other relevant parts of the framework are presented to novice users in a context-dependent manner. We have built FrUiT, an Eclipse plug-in that implements this approach and present a first assessment by mining parts of the Eclipse framework.

Detecting missing method calls in object-oriented software

When using object-oriented frameworks it is easy to overlook certain important method calls that are required at particular places in code. In this paper, we provide a comprehensive set of empirical facts on this problem, starting from traces of missing method calls in a bug repository. We propose a new system, which automatically detects them during both software development and quality assurance phases. The evaluation shows that it has a low false positive rate (<5%) and that it is able to find missing method calls in the source code of the Eclipse IDE.

IDE 2.0: collective intelligence in software development

Todays Integrated Development Environments (IDEs) only integrate the tools and knowledge of a single user and workstation. This neglects the fact that the way in which we develop and maintain a piece of software and interact with our IDE provides a rich source of information that can help ourselves and other programmers to avoid mistakes in the future, or improve productivity otherwise. We argue that, in the near future, IDEs will undergo a revolution that will significantly change the way in which we develop and maintain software, through integration of collective intelligence, the knowledge of the masses. We describe the concept of an IDE based on collective intelligence and discuss three example instantiations of such IDEs.

Maximum margin matrix factorization for code recommendation

Code recommender systems ease the use and learning of software frameworks and libraries by recommending calls based on already present code. Typically, code recommender tools have been based on rather simple rule based systems while many of the recent advances in Recommender Systems and Collaborative Filtering have been largely focused on rating data. While many of these advances can be incorporated in the code recommendation setting this problem also brings considerable challenges of its own. In this paper, we extend state-of-the-art collaborative filtering technology, namely Maximum Margin Matrix Factorization (MMMF) to this interesting application domain and show how to deal with the challenges posed by this problem. To this end, we introduce two new loss functions to the MMMF model. While we focus on code recommendation in this paper, our contributions and the methodology we propose can be of use in almost any collaborative setting that can be represented as a binary interaction matrix. We evaluate the algorithm on real data drawn from the Eclipse Open Source Project. The results show a significant improvement over current rule-based approaches.

On evaluating recommender systems for API usages

To ease framework understanding, tools have been developed that analyze existing framework instantiations to extract API usage patterns and present them to the user. However, detailed quantitative evaluations of such recommender systems are lacking. In this paper we present an automated evaluation process which extracts queries and expected results from existing code bases. This enables the validation of recommendation systems with large test beds in an objective manner by means of precision and recall measures. We demonstrate the applicability of our approach by evaluating an improvement of an existing API recommender tool that takes into account the framework-method context for recommendations.

Mining subclassing directives to improve framework reuse

To help developers in using frameworks, good documentation is crucial. However, it is a challenge to create high quality documentation especially of hotspots in white-box frameworks. This paper presents an approach to documentation of object-oriented white-box frameworks which mines from client code four different kinds of documentation items, which we call subclassing directives. A case study on the Eclipse JFace user-interface framework shows that the approach can improve the state of API documentation w.r.t. subclassing directives.

eAssignment: a case for EMF

Developing Eclipse plug-ins often involves the creation of data structures and corresponding data processing code. In developing eAssignment, an Eclipse-bases application to support electronic programming exercises, we identified several issues with implicit models of data structures and hand-written code needed to access them. In this paper, we report on our experiences of using the Eclipse Modeling Framework to overcome these shortcomings.

Welcome to the Fourth International Workshop on Search-driven Development: Users, Infrastructures, Tools, and Evaluation (SUITE 2012)

The fourth international workshop on Search-driven Development – Users, Infrastructure, Tools, and Evaluation (SUITE 2010) focuses on exploring the notion of search as a fundamental activity during software development, and all aspects that are related with integrating search into software development workflows. Two primary observations encouraged us to start this workshop series. First, results from existing research show that developers spend majority of their time in searching for code. Furthermore, developers have to struggle with a myriad of additional information needs such as those that are related with design or requirements documents or even with the communication between the various stakeholders in a development project. Second, recently there has been considerable effort from both academia and industry in building specialized search tools for software developers, in particular largescale code search engines.