The systematic interaction of software developers with the business domain experts
that are usually no software developers is crucial to software system maintenance and
creation and has surfaced as the big challenge of modern software engineering. Existing
frameworks promoting the typical programming languages with artificial syntax are
suitable to be processed by computers but do not cater to domain experts, who are used
to documents written in natural language as a means of interaction.Other frameworks
that claim to be fully automated, such as those using natural language processing, are
too imprecise to handle the typical requirements documents written in heterogeneous
natural language flavours. In this thesis, a framework is proposed that can support
the specification of business rules that is, on the one hand, understandable for nonprogrammers
and on the other hand semantically founded, which enables computer
processability. This is achieved by the novel language Adaptive Business Process and
Rule Integration Language (APRIL). Specifications in APRIL can be written in a style
close to natural language and are thus suitable for humans, which was empirically
evaluated with a representative group of test persons. A useful and uncommon feature
of APRIL is the ability to define reusable abstract mixfix operators as sentence patterns,
that can mimic natural language. The semantic underpinning of the mixfix operators
is achieved by customizable atomic formulas, allowing to tailor APRIL to specific
domains. Atomic formulas are underpinned by a denotational semantics, which is based
on Tempura (executable subset of Interval Temporal Logic (ITL)) to describe behaviour
and the Object Constraint Language (OCL) to describe invariants and pre- and postconditions.
APRIL statements can be used as the basis for automatically generating
test code for software systems. An additional aspect of enhancing the quality of
specification documents comes with a novel formal method technique (ISEPI) applicable
to behavioural business rules semantically based on Propositional Interval Temporal
Logic (PITL) and complying with the newly discovered 2-to-1 property. This work
discovers how the ISE subset of ISEPI can be used to express complex behavioural
business rules in a more concise and understandable way. The evaluation of ISE is done
by an example specification taken from the car industry describing system behaviour,
using the tools MONA and PITL2MONA. Finally, a methodology is presented that helps
to guide a continuous transformation starting from purely natural language business rule
specification to the APRIL specification which can then be transformed to test code. The
methodologies, language concepts, algorithms, tools and techniques devised in this work
are part of the APRIL-framework
