My research interests focus on the fields Software Engineering and Data Mining (applied to software-documents). In particular I'm interested in software diagnostics (quality assurance), refactoring (software evolution), experience-based resp. pattern-based Software Engineering and model-driven software development (MDSD). More specific I'm working on the diagnosis of quality defects (Antipatterns, Code Smells, Design flaws, etc.), the quality assurance in MDSD, plug-in based software development as well as knowledge discovery in code and defect repositories.
Quality Defects & Software Quality Assurance
This area of research is concerned with problems in software systems (esp. software products) that have a negative effect on the software quality. Today, a vast number of these defects are known and documented in various communities under various names. Typically, they are collected and described by practitioners and consult-ants and represent condensed experiences from multiple projects they were involved in. A systematic literature review conducted in 2007 revealed 43 different names that were used in the literature to describe these kind of problems - 22 of them with larger collections of quality defects.
The term "quality defect" is used as an umbrella term for the concepts antipattern, smell, flaw, pitfall, bug pattern, defect pattern, negative pattern, (bad) heuristic, (bad) characteristic, antiidiom, (design) problem, (design) defect, refactoring candidate, puzzlers, traps, anomalies, and many more (typically with an additional focus on a quality aspect, development phase, or abstraction level, e.g., performance antipatterns, test smells, or architectural anomalies) that have a negative effect on a quality aspect (e.g., maintainability, efficiency, or reusability).
DoctorQ:
An extensible plugin-system for the eclipse IDE to enable the analysis of software systems for the diagnosis of Quality Defects. The system is designed to diagnose quality defects during programming in order to assist the software developer directly in his work. This helps to prevent larger amount of work that might be necessary if the quality defects were diagnosed after programming (or an iteration in agile development) when more components are based on the flawed system and needed to be changed.
SQuaRE - The Software Quality Reference Architecture:
An extensible reference architecture for in-situ quality defect diagnosis and handling tools as realized by DoctorQ and VIDE-DD.
SQuaD:
The Software Quality Defect Ontology represents a systematic categorization of quality defects and associated concepts such as techniques for the diagnosis
of defects and the indication of treatments (e.g., refactorings).
Quality Defect Formalization:
One main part of the research is the unambiguous description of quality defects in order to apply them in knowledge based diagnosis systems as well as to
support the sometimes ambiguous descriptions found in the literature.
Quality Defect Diagnosis & Prioritization
The focus of this part of the research is the diagnosis of quality defects based on static, dynamic, and historic (e.g., versions in CVS) information as well as the systematic reduction of the amount of quality defects presented to the user (e.g., developer or maintainer).
Quality Defect Handling
After quality defects are discovery in a software system they need to be properly handled. This includes either the treatment and consequently the removal of the quality defect or the marking of the location and a decision that it was not applicable. Furthermore, this includes research on the context-specific contra-diagnosis based on specific locations (e.g., pattern roles and stereotypes). The persistent storage of these decisions can be supported by the annotation language RAL that was developed to record information about quality defects and their history in source code.
Refactoring configuration & Quality Defect Removal
If multiple quality defects are found at on location (e.g., a method) are diagnosed this has an effect on the indication of an optimal treatment plan (e.g., a sequence of refactorings).
Software Patterns & Anti-Patterns
AKAEM (Arbeitskreis Architektur- und Entwurfsmuster in der Fachgruppe Software-Architektur der GI): Der Arbeitskreis AKAEM dient der Diskussion und Erarbeitung von Prinzipien, Grundlagen, Methoden, Techniken, Werkzeugen für und Anwendungen von Mustern und Anti-patterns im Bereich der Software-Architektur. Der AK arbeitet aktuell schwerpunktmäßig an der Erstellung eines Musterkatalogs.
Pattern Aggregation:
An approach to systematically develop or "grow" software pattern based on experiences stored by developers in an Experience Management System (e.g., an Experience Factory) or a Defect Management Systems (e.g., bugzilla).
Model Driven Software Development & Software Architecture
Model-driven software development (MDSD) focuses on the idea of constructing software systems not by programming in a specific programming language but by designing models that are translated into executable software systems by generators. In theory, this process makes it unnecessary to care for an executable system's quality, as it is "optimized" by the generators. However, the designed models are also a work product that requires a minimal set of quality aspects (e.g., the maintainability of models over a longer life-time).
The goals of quality assurance for model-driven software development are diverse and include the improvement of quality aspects such as maintainability, reusability, security, or performance. Quality assurance for model-driven software development will play an important role for the future wide-spread usage of model-driven architectures in general, as well as in specific application domains.
SAE3D: Software Architecture Editor 3D (using PhysX and OpenGL)
Quality Defect Visualization in PIMs and PSMs
Intelligent Assistance
Intelligent assistance in software engineering is a relatively old research field that is nevertheless of high interest for
software engineers today. Giving support to the software engineers in programming, design, requirements, or other
software-related environments is necessary, as the work product is typically very complex, large, and influenced by many
persons. The core objective of intelligent assistance is to enable and improve the automation, insight, and interaction with
a software system through an IDE. One main topic for intelligent assistance was the context-specific diagnosis of quality
defects during development.
Intelligent Assistance for Software Engineering
Semantic Work Environments
SOP: The Software Organization Plattform is a plattform for software development organizations that is based on Mediawiki (
http://www.mediawiki.org), a free software wiki package originally written for Wikipedia.
Information and Software Visualization
Codigator: Source Code Visualization and Navigation. The visualization of information enables the faster assimilation of information by an observer. Therefore, the ad-hoc or post-hoc visualization of defect information during development or maintenance activities results in the easier comprehension by the software engineers. A first approach for visualization and visual navigation in source code together with quality defects was realized in the Codigator system. This prototype for a ad-hoc approach indicated that the visualization helps but the amount of information needs to be compressed or otherwise summarized.
Architecture Visualization in Model-driven Software Development
Quality Defect Visualization
Code Mining & Code Retrieval
Object oriented source code occurs in diverse programming languages with documentation using miscellaneous standards, comments in individual styles, or associated test cases that are hard to exploit through information retrieval or knowledge discovery techniques. Typically, the information about object-oriented source code for a software system is distributed across several different sources, which makes processing complex.
This area of research is concerned with problems regarding the retrieval, mining, and interconnection of all information concerning a software system.
COWA (Code Warehouse): The code warehouse acts as the repository for source code from several software systems for further processing. Source code in several versions from source versioning systems (e.g., CVS) of OSS projects is extracted, transformed (i.e., parsed), and loaded (i.e., stored) in the code warehouse. Currently only JAVA from CVS systems are parsed and stored in the code warehouse.
CORE (Code Retrieval Engine): The code retrieval engine is an application of the data from the code warehouse. It offers a search on the source code within the COWA using the lucene search engine. The index to be searched is build upon information from the code itself (e.g., class and method names) as well as information from the documentation (i.e., javadoc) and additional comments.
COAE (Code Analysis Engine): The code analysis engine is used to calculate metrics (and other countable characteristics) about the source code and its different versions for further analysis or mining purposes. Currently, only few metrics are calculated.
COME (Code Mining Engine): The code mining engine will be used to discover previously unknown knowledge about the source code using techniques from data, text, and web mining. Work in progress is using clustering techniques in order to discover library candidates from similar projects.
Biography for use in publications
Ultra-Short Biography (less than 25 words)
Jörg Rech is an entrepreneur as well as project manager and senior scientist at SAP Research Karlsruhe, Germany.
Contact him at Joerg.Rech@sap.com.
Short Biography (less than 100 words)
Jörg Rech is an entrepreneur and founder of the company Semantic Technologies, which is working on systems in the area of Web 2.0, Web 3.0 and the Semantic Web. Currently, he works as a senior scientist and project manager at SAP Research Karlsruhe, after managing several research projects at Fraunhofer IESE and the University of Kaiserslautern. He received a Ph.D. in Quality Defect Diagnosis from the University of Hildesheim and a M.S. (Diplom) in computer science from the University of Kaiserslautern. His research mainly concerned software engineering, semantic technologies, knowledge management, and data mining, where he published over fourty papers.
Contact him at Joerg.Rech@sap.com.
Medium Biography (less than 200 words)
Jörg Rech is a senior scientist and project manager at SAP Research Karlsruhe, Germany. He was a scientist and project manager at Fraunhofer IESE in Kaiserslautern and a research assistant at the software engineering research group (AGSE) of Prof. Dieter Rombach. Jörg received his Ph.D from the University of Hildesheim, Germany and his BSc (Vordiplom) his MSc (Diplom) in computer science with a minor in electrical science from the University of Kaiserslautern, Germany. His research mainly concerns context-sensitive diagnosis of quality-defects, their refactoring and treatment, semantic technologies in software organizations, experience and knowledge management, knowledge patterns, and software engineering, with a focus on model-driven software engineering. Previously he also researched code mining and code retrieval. Jörg authored over 40 international journal articles, book chapters, and refereed conference papers, mainly on software engineering and knowledge management, edited several books in the domain of software engineering and knowledge management, was the speaker of the GI working group on architectural and design patterns, and is a member of the German Computer Society (Gesellschaft für Informatik, GI).
Contact him at Joerg.Rech@sap.com.
Long Biography (less than 300 words)
Dr. Jörg Rech is a senior scientist and project manager at SAP Research Karlsruhe, the global technology research and innovation unit of SAP, in Germany. Previously, he worked for Fraunhofer IESE in Kaiserslautern and the University of Kaiserslautern, Germany. Dr. Rech worked on different German and European research projects in the areas of software engineering and knowledge management. In several industrial projects he was engaged with analyzing, evaluating, and improving the software development process and product for the diagnosis of defects and the indications of improvements. He has consulted for various European companies, including T-Com, empolis, and brainbot.
Dr. Rech received his Ph.D from the University of Hildesheim, Germany and his BSc (Vordiplom) his MSc (Diplom) in computer science with a minor in electrical science from the University of Kaiserslautern, Germany.
His research mainly concerns the context-sensitive diagnosis of quality-defects, their refactoring and treatment, semantic technologies in software organizations, experience and knowledge management, knowledge patterns, and software engineering, with a focus on model-driven software engineering. Previously he also researched code mining and code retrieval. Dr. Rech authored over 40 international journal articles, book chapters, and refereed conference papers, mainly on software engineering and knowledge management, edited several books in the domain of software engineering and knowledge management, was the speaker of the GI working group on architectural and design patterns, and is a member of the German Computer Society (Gesellschaft für Informatik, GI).
Contact him at Joerg.Rech@sap.com.
Back to top
