Edit DSS development: PLANFLOR.Description of DSS development

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DSS development

Software development methodology: How is the development process structured? 1) problem definition and tool requirement specification: What is the name of the tool design methodology? ; 2) Tool design: What are the main modelling techniques used to design the IT-solutions? ; 3) Construction: Use of version control, use of bug tracking system; 4) Tests: type of tests and procedures; 5) Implementation and training; 6) Maintenance: version management, feedback collection, 7) New developments
Methods applied in the design and requirements phase: Methods applied for the DSS design and requirements analysis
Types of software tests conducted: Description of the methods applied in the testing phase
Methods applied to ensure software quality: Description of the methods applied to ensure software quality. Particularly important in the case of quality certified software.
Development start year: Year when the Tool development started
Number of development years (100% equivalent): Number of people*allocation*Number of months
Development team size: Number of people, with at least 10% involvement of the project
Team profiles: For each type of participant on the development team, describe participation in different phases of the development process, role and expertise
Number of forest specialists in the development team: Number of different specialists considering their educational background
Number of users participating in specification: Total number of distinct stakeholders/users involved on the tool specification and development
Adaptation effort (man years): How the system will be adapted for a specific use, man years
+ +++Has KM tools applied to DSS development+++:

which KM techniques have been used during the development process of the described system, they are now maybe not part of the system, but were important during the development process. Agent: In the context of Data Mining (DM) agents are defined as promising techniques for retrieving information from databases. In the context of Knowledge Management (KM) agents are defined as software systems that learn how users work and provide assistance in their daily tasks. In the context of Artificial Intelligence (AI) and Expert Systems (ES) agents are small programs that reside on computers to conduct certain tasks automatically, they monitor the environment and react to certain trigger conditions. Expert Systems: There are various expert systems in which a rulebase and an inference engine are interlinked to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems a vast amount of knowledge is stored in the knowledge base. The knowledge base could consist of "if then" statements that resemble the sequence of mental steps that are involved in the human reasoning process. Artificial Intelligence techniques (e.g. ANN, Bayesian Networks, Logic Programming): AI has developed a large number of tools/techniques to solve the most difficult problems in computer science. E.g. Logic Programming is used for knowledge representation and problem solving, a number of techniques make use of probability theory to operate with incomplete or uncertain information (e.g. Bayesian)

+++Has stakeholder involvement+++:

KM tools used during the development of the DSS:
Stakeholder involvement:

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