Automated Test Code Generation Based on Formalized Natural Language Business Rules




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International Academy, Research and Industry Association (IARIA)



Peer reviewed



The paper addresses two fundamental problems in requirements engineering. The first one is the conflict between understandability for non-programmers and a semantically well-founded representation of business rules. The second one is the verification of productive code against business rules in requirements documents. As a solution, a language to specify business rules that are close to natural language and at the same time formal enough to be processed by computers is introduced. For more domain specific expressiveness, the language framework permits customizing basic language statements, so called atomic formulas. Each atomic formula has a precise semantics by means of predicate and Interval Temporal Logic. The customization feature is demonstrated by an example from the logistics domain. Behavioral business rule statements are specified for this domain and automatically translated to an executable representation of Interval Temporal Logic. Subsequently, the example is utilized to illustrate the verification of requirements by automated test generation based on our formalized natural language business rules. Thus, our framework contributes to an integrated software development process by providing the mechanisms for a human and machine readable specification of business rules and for a direct reuse of such formalized business rules for test-cases.


Work done with Ingolstadt University of Applied Sciences, Germany. Copyright (c) IARIA, 2012. URL for paper: . This is the original conference version of the article "Supporting test code generation with an easy to understand business rule language" in the International Journal on Advances in Software, vol. 6, no.1 & 2, 2013, pp. 69-79.


Requirements engineering, Business rules, Natural language, Testing, Logic


Bacherler, C., Moszkowski, B.C., Facchi, C. and Huebner, A. (2012) Automated Test Code Generation Based on Formalized Natural Language Business Rules. Proc. Seventh International Conference on Software Engineering Advances (ICSEA 2012), pp. 165-171


Research Institute