Philipp Schugerl

Mining Bug Repositories--A Quality Assessment

The process of evaluating, classifying, and assigning bugs to programmers is a difficult and time consuming task which greatly depends on the quality of the bug report itself. It has been shown that the quality of reports originating from bug trackers or ticketing systems can vary significantly. In this research, we apply information retrieval (IR) and natural language processing (NLP) techniques for mining bug repositories. We focus particularly on measuring the quality of the free form descriptions submitted as part of bug reports used by open source bug trackers. Properties of natural language influencing the report quality are automatically identified and applied as part of a classification task. The results from the automated quality assessment are used to populate and enrich our existing software engineering ontology to support a further analysis of the quality and maturity of bug trackers.

SE-CodeSearch: A scalable Semantic Web-based source code search infrastructure

Available code search engines provide typically coarse-grained lexical search. To address this limitation we present SE-CodeSearch, a Semantic Web-based approach for Internet-scale source code search. It uses an ontological representation of source code facts and analysis knowledge to complete missing information using inference engine. This approach allows us to reason and search across project boundaries containing often incomplete code fragments extracted in a one-pass and no-order manner. The infrastructure provides a scalable approach to process and query across large code bases mined from software repositories and code fragments found online. We have implemented our SE-CodeSearch as part of SE-Advisor framework to demonstrate the scalability and applicability of our Internet-scale code search in a software maintenance context.

Scalable clone detection using description logic

The semantic web is slowly transforming the web as we know it into a machine understandable pool of information that can be consumed and reasoned about by various clients. Source-code is no exception to this trend and various communities have proposed standards to share code as linked data. With the availability of large amounts of open source code published in publically accessible repositories and the introduction of massively horizontally scaling frameworks and cloud computing infrastructure, a new era of software mining across information silos is reshaping the software engineering landscape. The so far unreachable goal of analyzing code at a global level, and therefore detecting global software clones, has become manageable. Description logic and semantic web reasoners have so far only plaid a minor role in this transformation and are mainly used to model source code data. In this paper, we introduce a clone detection algorithm that uses a semantic web reasoner and is based on the Hadoop map-reduce framework that can scale horizontally to a large amount of data. We also define a novel and compact clone model that only considers control-blocks and used data types while still yielding similar clone detection results than more complex representations. In order to validate our approach we have compared our algorithm to some of the leading clone detection tools (CCFinder, JCD and Simian) and show differences in performance and detection precision.The semantic web is slowly transforming the web as we know it into a machine understandable pool of information that can be consumed and reasoned about by various clients. Source-code is no exception to this trend and various communities have proposed standards to share code as linked data. With the availability of large amounts of open source code published in publically accessible repositories and the introduction of massively horizontally scaling frameworks and cloud computing infrastructure, a new era of software mining across information silos is reshaping the software engineering landscape. The so far unreachable goal of analyzing code at a global level, and therefore detecting global software clones, has become manageable. Description logic and semantic web reasoners have so far only plaid a minor role in this transformation and are mainly used to model source code data. In this paper, we introduce a clone detection algorithm that uses a semantic web reasoner and is based on the Hadoop map-reduce framework that can scale horizontally to a large amount of data. We also define a novel and compact clone model that only considers control-blocks and used data types while still yielding similar clone detection results than more complex representations. In order to validate our approach we have compared our algorithm to some of the leading clone detection tools (CCFinder, JCD and Simian) and show differences in performance and detection precision.

Beyond generated software documentation — A web 2.0 perspective

Over the last decades, software engineering processes have constantly evolved to reflect cultural, social, technological, and organizational changes, which are often a direct result of the Internet. The introduction of the Web 2.0 resulted in further changes creating an interactive, community driven platform. However, these ongoing changes have yet to be reflected in the way we document software systems. Documentation generators, like Doxygen and its derivatives (Javadoc, Natural Docs, etc.) have become the de-facto industry standards for creating external technical software documentation from source code. However, the inter-woven representation of source code and documentation within a source code editor limits the ability of these approaches to provide rich media, internationalization, and interactive content. In this paper, we combine the functionality of a web browser with a source code editor to provide source code documentation with rich media content. The paper presents our fully functional implementation of the editor within the Eclipse framework.

Reasoning about Global Clones: Scalable Semantic Clone Detection

The Semantic Web is slowly transforming the Web as we know it into a machine understandable pool of information that can be consumed and reasoned about by various clients. Source code is no exception to this trend and various communities have proposed standards to share code as linked data. With the availability of large amounts of open source code published in publicly accessible repositories, the introduction of massive horizontal scaling frameworks, and cloud computing infrastructures, a new era of software mining across information silos is reshaping the software engineering landscape. Given these technological advances, analyzing code at a global scale, across systems, projects and organizational boundaries, becomes feasible. In this paper, we introduce a clone detection algorithm and its implementation that can scale to such large global datasets, by modeling clones using description logic and applying a horizontal scaling Semantic Web reasoner. We demonstrate how our simple feature vector that only uses control statements, data types and method calls, can yield results similar to other popular clone detection tools. Our approach does not only allow us to reliably identify clones in a global context. By using a semantic reasoner, it also allows us to expand clone detection to a new class of semantic clones. We have compared our algorithm to some of the leading clone detection tools (DECKARD, CCFinder, JCD, and Simian) in order to validate our approach and show the differences in detected clones and performance.

A Contextual Guidance Approach to Software Security

With the ongoing trend towards the globalization of software systems and their development, components in these systems might not only work together, but may end up evolving independently from each other. Modern IDEs have started to incorporate support for these highly distributed environments, by adding new collaborative features. As a result, assessing and controlling system quality (e.g. security concerns) during system evolution in these highly distributed systems become a major challenge. In this research, we introduce a unified ontological representation that integrates best security practices in a context-aware tool implementation. As part of our approach, we integrate information from traditional static source code analysis with semantic rich structural information in a unified ontological representation. We illustrate through several use cases how our approach can support the evolvability of software systems from a security quality perspective.

An ontological guidance model for software maintenance

Software maintenance is a multi-dimensional problem that involves the integration, abstraction, and analysis of different knowledge resources and artifacts. In todays global software projects, artifacts tend to be distributed across various physical and virtual locations. This often leads to situations in which maintainers are left with no guidance in locating and using resources relevant to complete a particular task. In this research, we present a software engineering environment that provides context-aware guidance to developers. SE knowledge and artifacts such as processes, workflows, bugs, revisions and social structures are modeled as part of a uniform ontological representation. This representation eliminates traditional boundaries between knowledge resources and provides the basis for knowledge inference. Consequently, given a current maintenance context a developer is guided in his specific task by making knowledge available when needed.

Quality Validation through Pattern Detection - A Semantic Web Perspective

Given the ongoing trend towards the globalization of software systems, open networks, and distributed platforms, validating non-functional requirements and quality becomes essential. Our research addresses this challenge from two different perspectives: (1) the integration of knowledge and tool resources through Semantic Web technologies as part of our SE-PAD environment, in order to reduce or eliminate existing traditional information and analysis silos. (2) The ability to reason upon linked resources to infer both explicit and implicit patterns to support the validation of quality aspects. We have applied our SE-PAD environment for the detection of security and design patterns, as well as the violations of secure programming guidelines.