Supporting software engineering with knowledge acquisition, formalization, and application

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Software development is a design process where every involved person has to make a number of decisions, each of them with several possible choices. These decisions concern different aspects e.g., designing, implementing, testing, maintaining, etc. Software practitioners need support for deciding which alternative is better for their specific context. Improving the ability to develop "good" software implies activities that analyze results from several previous projects in order to identify similarities and differences between them. The results of these analyses can be formulated as knowledge. Examples of such knowledge are patterns, heuristics, best practices, assessment models, etc.We consider that software engineering performances could be improved if knowledge generated during software developments and projects is captured, structured, and formalized. However, a lot of knowledge is implicit, residing in the brains of the software practitioners or engineers. This fact makes knowledge elicitation, acquisition, and formalization a challenge.In this context, we believe that:1) Different sources and types of knowledge can be handled by various approaches, and can support software practitioners at different stages of the software life cycle; for instance, for identifying relevant patterns and pattern combinations, promoting reusability, making design decisions, fixing software defects found during testing, analyzing change impact during maintenance, etc.2) We need to be supported by appropriate technology and tools. Artificial intelligence techniques are being used for knowledge discovery, acquisition, and formalization; for instance for defining/using various ontologies, for knowledge representation and utilization, machine-learning, formal concept analysis, etc.