Artificial Intelligence and SoftwareEngineering: Status and Future Trends Jörg Rech, Klaus-Dieter AlthoffThe disciplines of Artificial Intelligence and Software Engineering have many commonalities. Both deal with modelingreal world objects from the real world like business processes, expert knowledge, or process models. This article givesa short overview about these disciplines and describes some current research topics against the background of com-mon points of contact. Aspects of Artificial Intelligence1 Introduction There is a general agreement in the AI community that the discipline of AI was born at the Dartmouth conference in During the last decades the disciplines of Artificial Intelli- 1956. According to Winston  “AI is the study of the com-gence (AI) and Software Engineering (SE) have developed putations that make it possible to perceive, reason, and act”.separately without much exchange of research results. In AI Wachsmuth  assumes this definition and points out that,we researched techniques for the computations that made it “AI differs from most of psychology because of its greaterpossible to perceive, reason, and act. Research in SE was emphasis on computation, and it differs from most of com-concerned with supporting human beings to develop better puter science because of its greater emphasis on percep-software faster. tion, reasoning, and action”. As a field of academic study, Today, several research directions of both disciplines many AI researchers reach to understand intelligence bycome closer together and are beginning to build new re- becoming able to produce effects of intelligence: intelligentsearch areas. Software Agents play an important role as behavior. One element in AI’s methodology is that progressresearch objects in Distributed AI (DAI) as well as in agent- is sought by building systems that perform: synthesis beforeoriented software engineering (AOSE). Knowledge-Based analysis . “Systems are good science”, as Hendler saidSystems (KBS) are being investigated for learning software . Or more drastically by Wachsmuth : “it is not theorganizations (LSO) as well as knowledge engineering. aim of AI to build intelligent machines having understoodAmbient Intelligence (AmI) is a new research area for dis- natural intelligence, but to understand natural intelligence bytributed, non-intrusive, and intelligent software systems both building intelligent machines”. Even more strikingly Aaronfrom the direction of how to build these systems as well as Sloman puts it this way (by citing his colleague Russelhow to design the collaboration between ambient systems. Beale): "AI can be defined as the attempt to get real ma-Last but not least, Computational Intelligence (CI) plays an chines to behave like the ones in the movies”. In addition,important role in research about software analysis or project he points out that AI has two main strands, a scientificmanagement as well as knowledge discovery in databases strand and an engineering strand, which overlap considera-or machine learning. bly in their concepts, methods, and tools, though their objec- Furthermore, in the last five to ten years several books, tives are very different.journals, and conferences have focused on the intersection This view is supported by Wahlster  who clarifies thatbetween AI and SE. The international conference and asso- AI has two different types of goals, one motivated by cogni-ciated journal Automated Software Engineering (ASE) pre- tive science, the other by the engineering sciences (cf.sents research about formal and autonomic approaches to Figure 1).support SE . Similar topics with a stronger focus on KBSand knowledge management are published in the interna- Engineering Sciencestional conference and associated journal of Software Engi- Bio Sciencesneering and Knowledge Engineering (IJSEKE) . Psychology In this paper, we give a short overview about the statusand future trends in the intersection between AI and SE. We Computer Cognitive AIfocus on the topics software agents, KBS, AmI, and CI as Science Sciencethe areas covered by the contributions of this special issue.In Section 2 we describe the disciplines AI and SE. The Philosophy Linguisticsfocused topics are described in more detail in Section 3.Finally, in Section 4 we give an outlook for the next yearsand present new challenges for both disciplines. Figure 1 AI and Related Research Areas (adapted from )2 Artificial Intelligence and Software En- A further sub-division (adapted from Richter  and gineering Abecker ) of AI into sub-fields, methods, and techniques is shown in Figure 2. This section will shed some light on the disciplines AIand SE for those not familiar with the other discipline.
2 Vision & Image chosen technology can be used for improved versions of the Natural Language Understanding Neural Networks Understanding & Dictating SE method. Machine Learning Mathematical Knowledge Aspects of Software Engineering Reasoning Representation Pattern Expert Systems The discipline of SE was born 1968 at the NATO confer- Knowledge Logical & Recognition ence in Garmisch-Partenkirchen, Germany [52, 71] where Games Engineering approximative Robotics the term “SE crisis” was coined. Its main concern is the Heuristic reasoning Search Problem efficient and effective development of high-qualitative and Learning from experience Planning Agents Solving mostly very large software systems. The goal is to supportAI Fields AI Methods and Techniques AI Fields software engineers and managers in order to develop better software faster with (intelligent) tools and methods.Figure 2 AI Fields, Methods, and Techniques Since its beginning several research directions devel- oped and matured in this broad field. Figure 4 shows the For SE the scientific strand orientating towards cognitive software development reference model integrating importantscience and humanities in general could be a helpful guid- phases in a software lifecycle. Project Engineering is con-ance for interdisciplinary research. Of course, there is a cerned with the acquisition, definition, management, moni-strong overlap between SE and the engineering strand of toring, and controlling of software development projects asAI. An important part of the latter are KBS. well as the management of risks emerging during project Richter  defines three different levels as essential for execution. Methods from Requirements Engineering aredescribing KBS: the cognitive layer (human-oriented, ra- developed to support the formal and unambiguous elicitationtional, informal), the representation layer (formal, logical), of software requirements from the customers, to improve theand the implementation layer (machine-oriented, data struc- usability of the systems, and to establish a binding andtures and programs). These levels are shown in Figure 3. unambiguous definition of the resulting system during andBetween the knowledge utterance and its machine utiliza- after software project definition. The research for Softwaretion several transformations have to be performed (thick Design & Architecture advances techniques for the devel-arrows). They point to the direction of increased structuring opment, management, and analysis of (formal) descriptionswithin the layers and proceed from the cognitive form to a of abstract representations of the software system as wellmore formal and more efficiently processed form. The letter as required tools and notations (e.g., UML). Techniques toA is a reminder for Acquisition (which is human-oriented) support the professional Programming of software are ad-while C is a shorthand for Compilation (machine-oriented). vanced to develop highly maintainable, efficient, and effec-Each syntactic result in the range of a transformation be- tive source code. Verification & Validation is concerned withtween layers has to be associated with the meaning in the the planning, development, and execution of (automated)domain of the transformation. The most interesting and tests and inspections (formal and informal) in order to dis-difficult arrow is the inverse transformation back to the cog- cover defects or estimate the quality of parts of the software.nitive layer; it is usually called explanation. Research for Implementation & Distribution is responsible for the development of methods for the introduction at the customer’s site, support during operation, and integration in A Cognitive Layer existing IT infrastructure. A After delivery to the customer software systems typically switch into a Software Evolution phase. Here the focus of C Representation Layer research lies on methods in order to add new and perfect existing functions of the system. Similarly, in the parallel C phase Software Maintenance techniques are developed for the adaptation to environmental changes, prevention of Implementation Layer foreseeable problems, and correction of noticed defects. If the environment changes dramatically or further enhance-Figure 3 The Three Levels of Knowledge-Based Systems ments are impossible the system either dies or enters a Reengineering phase. Here techniques for software under- Why is AI interesting for researchers from SE? It can standing and reverse engineering of software design areprovide the initial technology and first (successful) applica- used to port or migrate a system to a new technology (e.g.,tions as well as a testing environment for ideas. The inclu- from Ada to Java or from a monolithic to a client/serversion of research supports the enabling of human-enacted architecture) and obtain a maintainable system.processes and increases user acceptance. AI technology Since the eighties the systematic reuse and manage-can help to base the overall SE method on a concrete tech- ment of experiences, knowledge, products, and processesnology, providing sufficient detail for the initial method de- was developed and named Experience Factory (EF) .scription, and through the available reference technology This field, also known as Learning Software Organizationclarifying the semantics of the respective method. In addi- (LSO), researches methods and techniques for the man-tion, other AI techniques naturally substituting/extending the agement, elicitation, and adaptation of reusable artifacts from SE projects.
3 Development Project n Maintenance Project n analysis techniques. Knowledge Acquisition (KA) techniques Development Project 1 Maintenance Project 1  help to build EF and intelligent ambient systems like Project Monitoring & Management Domain Modeling (DM) techniques support the construction Requirement Analysis Re-engineering of requirements for software systems and product lines. Case-based Reasoning (CBR) is used to support the re- Verification & Validation trieval and management of data in EF. Information Agents Project Planning Evolution & are used in SE to simulate development processes or to Design Testing Maintenance distribute and explain change requests. Agent-Oriented Software Engineering Distribution & Software Agents are typically small intelligent systems Programming Support Implementation that cooperate to reach a common goal. These agents are a Quality Assurance & Management relatively new area where research from KI and SE inter- sects. From the AI side the focus in this field lies on even more intelligent and autonomous systems to solve more complex problems using communication languages between agents. In SE agents are seen as systems that need more Experience Base Project Database n • Experiences about Processes, Project Database 1 or less specialized formal methods for their development, Products, Technologies, … • Software Documentation • Reusable models, processes, • Measured Data verification, validation, and maintenance. products, plans, … Agent-Oriented Software Engineering (AOSE) (a.k.a. Experience Factory Agent Based Software Engineering (ABSE)) as related to object-oriented SE (OOSE) is centered around systemsFigure 4 Software Development Reference Model where objects in a model of a software system are intelli- gent, autonomous, and proactive. Currently the systematic Why is SE for AI researchers interesting? It supports development and representation of software agents is re-systematic AI application development, the operating of AI searched and languages for their representation duringapplications in real-life environments, as well as evaluating development, like the Agent UML , were created. For(e.g., ), maintaining (e.g., ), continuously improving, example, several methods like MASSIVE by Lind , GAIAand systematically comparing them with alternative ap- by Wooldridge et al. , MESSAGE by Caire et al. ,proaches (e.g., another modeling method). SE also supports TROPOS by Castro et al. , or MAS-CommonKADS bythe systematic definition of the respective application do- Iglesias et al.  were developed.mains, e.g., through scoping methods . Agents and AOSE are applied in many areas like intelli- gent and agent-based user interfaces to improve system3 Intersections between AI and SE usability, trading agents in eCommerce to maximize profits, or assisting agents in everyday work to automate common tasks (e.g., booking hotel rooms) . Furthermore software While the intersections between AI and SE are currently agents are increasingly used to simulate real world domainsrare they are multiplying and growing. First points of contact (e.g., traffic control) or work processes in SE. But agentemerged from the application of techniques from one disci- technology is not a shiny new paradigm without problems –pline to the other . some pitfalls for AOSE are described by Wooldridge in . Today, methods and techniques from both disciplines A state of the art survey about agent-oriented SE bysupport the practice and research in the respectively other Tveit summarized previous publications and methods .research area. Figure 5 depicts some research areas in AI Formal methods for the specification and verification ofand SE as well as their intersections. systems were developed in order to describe, proof, and check the goals, beliefs, and interaction of agents and agent Artificial Intelligence Software Engineering systems [82, 83]. Today, more and more workshops are being organized KA AmI DM RE covering the common ground of intelligent agents and SE. Examples for communities in this field are the Workshop Learning CI Natural Language Understanding & Dictating Mathematical Vision & Image Understanding Knowledge Neural Networks Machine Learning PM DE Software Engineering for Large-Scale Multi-Agent Systems (SELMAS 2004) , the International Workshop Series on Reasoning Representation Expert Systems Pattern Knowledge Engineering Logical & Recognition Robotics Agents AOSE DAI CE Games approximative reasoning Heuristic Learning from Search Problem experience Planning Agents Solving Agent-Oriented Software Engineering (AOSE 2004) , the AI Fields AI Methods and Techniques AI Fields Fig. 2 KBS EF CBR International Workshop on Agent-Oriented Methodologies (AO 2003), International Workshop on Radical Agent Con- cepts (WRAC 2003) , or the International Joint Confer-Figure 5 Research Areas in AI and SE and their Intersections ence on Autonomous Agents & Multi-Agent Systems (AAMAS 2004) . Workshops on more formal aspects of Systematic software development (including Require- AOSE are FAABS or KIMAS [36, 37].ments Engineering (RE), Engineering of Designs (DE), or As summarized in the Agent Technology Roadmap bysource code (CE)) or project management (PM) methods the AgentLink network future research is required to enablehelp to build intelligent systems while using advanced data agent systems to adapt autonomously to communication
4languages, behavioral patterns, or to support the reuse of An overview on relevant approaches for knowledge-knowledge bases . based SE is given in [11, 13, 27, 28, 43, 54, 73]. Relevant events are part of the LSO, SEKE, ASE, and CBRKnowledge-Based Software Engineering (www.iccbr.org) event series (as well as the corresponding SE is a highly dynamic field in terms of research and journals including the International Journal on Knowledgeknowledge, and it depends heavily upon the experience of and Information Systems (KAIS)). Conferences and work-experts for the development and advancement of its meth- shops (again as well as journals) on knowledge manage-ods, tools, and techniques. For example, the tendency to ment are of interest if they have a concrete relationship todefine and describe "best practices" or "lessons learned" is software-related issues. Further relevant events are thequite distinctive in the literature . As a consequence, it Joint Conference on Knowledge-Based SE (JCKBSE) 2002was the SE field where an organization, the EF, was intro-  and 2004, and the workshops on Knowledge Orientedduced that was explicitly responsible to systematically deal Maintenance (KOM 2004), or Knowledge-Based Object-with experience. An EF is a logical and/or physical infra- Oriented SE (KBOOSE 2002).structure for continuous learning from experience and in-cludes an experience base (EB) for the storage and reuse of Computational Intelligence & KDDknowledge. The EF approach was invented in the mid- The research area CI has recently observed an increas-eighties . As practice shows, it is substantial for the ing interest from researchers of both disciplines. Techniquessupport of organizational learning that the project organiza- like neural networks, evolutionary algorithms, or fuzzy sys-tion and the learning organization are separated  (see tems are increasingly applied and adapted for specific SEalso Figure 4). problems. They are used to estimate the progress of pro- The initial example for an operating EF was the NASA jects to support software project management , for theSE Laboratory . In the meantime EF applications were discovery of defect modules to ensure software quality, or todeveloped in the USA and also in Europe [8, 73]. The great plan software testing and verification activities to minimizeamount of successful EF applications gave the ignition to the effort for quality assurance [41, 44, 56].study LSOs more intensively regarding the methodology for In a state-of-the-art survey about KDD in SE, Mendoncabuilding up and running an EF . This also includes the and Sunderhaft summarized previous publications as welldefinition of related processes, roles, and responsibilities as several mining techniques and tools . One of the firstand, last but not least, the technical realization. The most publications that explicitly collected contributions to KDD fordetailed methodology for the build-up of an EF/EB on pro- SE data was the IJSEKE Special Issue in 1999 . Moreject knowledge also for the presentation of the according and more workshops are being organized on CI and SE.processes is given in , an extension concerning evalua- Examples for workshops are WITSE (Intelligent Technolo-tion, maintenance, and architecture can be found in . gies for SE), MSR (Mining Software Repositories), DMSK EF is increasingly emerging towards a generic approach (Data Mining for SE and Knowledge Engineering), andfor reuse of knowledge and especially experience. This SCASE (Soft Computing applied to SE).includes also applications independent of the SE domain, Today, many application areas for KDD in SE have beene.g., supporting the continuous improvement process in established in fields like quality management, project man-hospitals , the field of help-desk and service support , agement, risk management, software reuse, or softwareand the management of "non-software"-projects . Future maintenance. For example, Khoshgoftaar et al. applied andtrends in the scope of EF include the detailing of all neces- adapted classification techniques to software quality datasary policies, validation, and empirical evaluation [17, 73], . Dick researched determinism of software failures withgaining experience with the technical realization of huge time series analysis and clustering techniques . CookEFs , the integration with the according business proc- and Wolf used the Markov approach to mine process andesses , and the running of EFs . workflow models from activity data . Pozewauning ex- In the areas of Cognitive Science and AI, CBR emerged amined the discovery and classification of component be-in the late seventies and early eighties as a model for hu- havior from code and test data to support the reuse of soft-man problem solving and learning [65, 66]. In AI, this led to ware . Michail used association rules to detect reusea focus of KBS on experience (case-specific knowledge) in patterns (i.e., typical usage of classes from libraries) . Asthe late eighties and beginning nineties, mostly in the form an application of KDD in software maintenance, Shirabadof problem-solution cases . Since several years there developed an instrument for the extraction of relationships inhas been a strong tendency in the CBR community  to software systems by inductive methods based on data fromdevelop methods for dealing with more complex applica- various software repositories (e.g., update records, version-tions. One example is the use of CBR for KM , another ing systems) to improve impact analysis in maintenanceone is its use for SE . A very important issue here is the activities . Zimmermann and colleagues pursue theintegration of CBR with EF: Since the mid-nineties CBR is same goal using a technique similar to CBR. In order toused both on the organizational EF process level as well as support software maintainers with related experiences inthe technical EB implementation level [12, 35, 74]. Mean- form of association rules about changes in software systemswhile this approach establishes itself more and more [7, 20, they mined association rules from a versioning system by40]. Usually product- and process-oriented approaches are collecting transactions including a specific change .used independently from each other, or as alternatives. As a Morasca and Ruhe built a hybrid approach for the predictionfirst step a deep integration of the approaches of EF and of defect modules in software systems with rough sets andCBR has been achieved [7, 11, 54, 73]. logistic regression based on several metrics (e.g., LOC) .
5 Future research in this field is required to analyze formal develop covering knowledge-based systems for learningproject plans for risk discovery, to acquire project informa- software organizations, the development of computationaltion for project management, or directly mine software rep- intelligence and knowledge discovery techniques for soft-resentations (e.g., UML, sourcecode) to detect defects and ware artifacts, agent-oriented SE, or professional develop-flaws early in development. ment of ambient intelligence systems. As a conclusion, we have identified several researchAmbient Intelligence fields interesting for both disciplines. The articles in the The idea behind AmI are sensitive, adaptive, and reac- remainder of this special issue elaborate on specific re-tive systems that are informed about the user’s needs, hab- search problems in these intersections.its, and emotions in order to support them in their daily work. Therefore, techniques for autonomous, intelligent,robust, and self-learning systems are needed to enable Referencescommunication between systems (machine-machine inter-faces and ontologies) or users and systems (human-  SEKE 2002: the 14th International Conference onmachine interfaces). Software Engineering and Knowledge Engineering. Is- AmI is based on several research areas, like ubiquitous chia, Italy: New York: ACM, 2002.and pervasive computing, intelligent systems, and context  Proceedings of the 18th IEEE International Conferenceawareness . Research for AmI tries to build an environ- on Automated Software Engineering (ASE). Montreal,ment similar to the previously mentioned research areas like Quebec, Canada: IEEE Computer Society, ISBN: 0-intelligent software agents, KBS, as well as knowledge dis- 769-52035-9, 2003.covery (e.g. to detect and analyze foreign systems and  Aarts E. H. L., Ambient intelligence: First Europeansoftware). symposium (EUSAI 2003), vol. 2875. Veldhoven, The There are several AI research areas for the development Netherlands: Springer-Verlag, 3540204180, 2003.of smart algorithms for AmI applications , e.g., userprofiling, context awareness, scene understanding , or  Abecker A., "Wissensbasierte Systeme," Berufsaka-planning and negotiation tasks . Research from the SE demie Mosbach, Lecture Notes 2002.side is concerned with model-driven development for mobile  Aha D. W., Becerra-Fernandez I., Maurer F., and Mu-computing , the verification of mobile code , the ñoz-Avila H., Exploring synergies of knowledge: man-specification of adaptive systems , or the design of em- agement & case-based reasoning. Menlo Park, Calif.:bedded systems . Additionally, we need intelligent hu- AAAI Press, ISBN: 1-577-35094-4, 1999.man interfaces from a usability perspective that translate  Althoff K.-D., Evaluating Case-Based Reasoning Sys-between users and a new configuration of the ambient sys- tems: The Inreca Case Study. Postdoctoral Thesistem. A fusion of these two fields could be established in (Habilitationsschrift). Dept. of Computer Science, Uni-order to analyze and evaluate foreign software systems that versity of Kaiserslautern, Kaiserslautern, Germany,try to connect with the own system to be executed on its 1997.hardware.  Althoff K.-D., "Case-based reasoning," in Handbook of Currently, research for AmI is primarily funded by the EU software engineering & knowledge engineering, vol. 1,as well as the German National Science Foundation (DFG). S.-K. Chang, Ed. Singapore: World Scientific, 2001,For example, several scenarios describing the vision of the pp. 549-587.EU were published in , and the integrated project  Althoff K. D., Becker K. U., Decker B., Klotz A., Leo-WearIT@Work is funded at the University of Bremen which pold E., Rech J., and Voss A., "The indiGo project: en-emphasizes AmI for work processes. In Germany, the DFG hancement of experience management and processfunded the „Forschungsschwerpunkt Ambient Intelligence“ learning with moderated discourses." Berlin, Germany:at the University of Kaiserslautern (http://www.eit.uni- Springer Verlag, 2002, pp. 53-79.kl.de/AmI). Various workshops and conferences on AmI were estab-  Althoff K.-D., Bomarius F., Müller W., and Nick M.,lished to foster exchange about AmI. Examples for these "Using a Case-Based Reasoning for Supporting Con-meetings are the European Symposium on Ambient Intelli- tinuous Improvement Processes," presented at Ger-gence (EUSAI) , Workshop on Ambient Intelligence man Workshop on Machine Learning, Leipzig, 1999.(WAI), Workshop on Ambient Intelligence for Scientific Dis-  Althoff K.-D., Decker B., Hartkopf S., Jedlitschka A.,covery (AMDI), Workshop on Ambient intelligence @ Work, Nick M., and Rech J., "Experience Management: TheInternational Conference on Ubiquitous Computing (Ubi- Fraunhofer IESE Experience Factory," presented atComp) , or the Workshop on Agents for Ubiquitous Industrial Conference Data Mining, Leipzig, 2001.Computing (UbiAgents).  Althoff K.-D. and Nick M. M., "How to Support Ex- perience Management with Evaluation - Foundations,4 Outlook Evaluation Methods, and Examples for Case-Based Reasoning and Experience Factory." Berlin: Springer Verlag, 2004 (forthcoming, Accepted for LNAI series). 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8 Truszkowski W., Rouff C., and Hinchey M., Proceed- Jörg Rech ings of the first international workshop on Radical Fraunhofer IESE Agent Concepts (WRAC 2002), vol. 2564. McLean, Institute for Experimental Software Engineering VA, USA: Springer, 3-540-40725-1, 2003. 67661 Kaiserslautern, Germany Tveit A., "A survey of Agent-Oriented Software Engi- email@example.com neering," presented at Proc. of the First NTNU CSGS Conference, 2001. Klaus-Dieter Althoff received Ph.D. and Verhaegh W. F. J., Aarts E. H. L., and Korst J., Algo- Habilitation in computer science from the rithms in ambient intelligence, vol. 2. Boston: University of Kaiserslautern, Germany. He Dordrecht, ISBN: 140201757X, 2004. was/is responsible for a number of research Wachsmuth I., "The Concept of Intelligence in AI," in projects on knowledge-based systems, Prerational Intelli-gence - Adaptive Behavior and Intel- machine learning, case-based reasoning, ligent Systems without Symbols and Logic, vol. 1, H. knowledge management (KM), and experi- Cruse, D. J., and RitterH., Eds. Dordrecht, The Nether- ence factory. He was/is member of various lands: Kluwer Academic Publishers, 2000, pp. 43-55. program committees, reviewer for many Wahlster W., "Einführung in die Methoden der Künstli- scientific journals, and co-chair of a number chen Intelligenz," University of Saarbrücken, Germany, of international events, e.g., the 3rd Confer- Lecture Notes 2002. ence on Professional KM (WM 2005). From Welzer T., Yamamoto S., and Rozman I., Proceedings 1997 to 2004 he was responsible for Experi- of the fifth Joint Conference on Knowledge-Based ence-based Systems and Processes at the Software Engineering. Amsterdam: Washington DC, Fraunhofer Institute for Experimental Soft- ISBN: 1586032747, 2002. ware Engineering as group leader/depart- Winston P. H., Artificial intelligence, 3rd (repr. with ment head. Since April 2004, he represents corrections 1993) ed. Reading, Mass.: Addison- the chair for Data and Knowledge Manage- Wesley, ISBN: 0-201-53377-4, 1993. ment at the University of Hildesheim. Wooldridge M., "Agent-based software engineering," IEE Proceedings Software Engineering, vol. 144, pp. Jörg Rech studied Computer Science at the 26-37, 1997. University of Kaiserslautern. He received the B.S. (Vordiplom) as well as the M.S. (Dip- Wooldridge M. and Ciancarini P., "Agent-oriented lom) in computer science with a minor in software engineering: the state of the art," presented at electrical science from the University of 1st International Workshop on Agent Oriented Soft- Kaiserslautern, Germany. He was a re- ware Engineering (AOSE 2000), Berlin, Germany, search assistant at the software engineering 2000. research group (AGSE) led by Prof. Dieter Wooldridge M., Jennings N. R., and Kinny D., "The Rombach at the university of Kaiserslautern. Gaia methodology for agent-oriented analysis and de- Currently, he is a researcher and project sign," Autonomous Agents and Multi Agent Systems, manager at the Fraunhofer Institute for Ex- vol. 3, pp. 285-312, 2000. perimental Software Engineering. His re- Wooldridge M. J. and Jennings N. R., "Software engi- search mainly concerns knowledge discov- neering with agents: pitfalls and pratfalls," IEEE Inter- ery in software repositories, defect discov- net Computing, vol. 3, pp. 20-7, 1999. ery, code mining, code retrieval, automated Zimmermann T., Weißgerber P., Diehl S., and Zeller code reuse, software analysis, and knowl- A., "Mining Version Histories to Guide Software edge management. Changes," presented at 26th International Conference on Software Engineering (ICSE), Edinburgh, UK, 2004.ContactDr. habil. Klaus-Dieter AlthoffData and Knowledge ManagementUniversity of HildesheimPO Box 10136331113 Hildesheim, Germanyalthoff@dwm.uni-hildesheim.de