Ensuring conformance as well as establishing quality, software testing is anintegral part of software engineering lifecycle. However, due to resource and time-to-marketconstraints, testing all exhaustive possibilities is impossible in nearly all practical testingproblems. Considering the aforementioned constraints, much research is now focusing on asampling technique based on interaction testing (termed t-way strategy). Although helpful,most existing t-way strategies (e.g. AETG, IPOG and GTWay) assume that all parametershave uniform interaction. However, in reality, the interaction between parameters is rarelyuniform. Some parameters may not even interact rendering wasted testing efforts. As a result,a number of newly developed t-way strategies that considers variable strength interactionbased on input-output relationships have been developed in the literature e.g. Union,ParaOrder and Density. Although useful, these strategies often lack in optimality i.e. in termof the generated test size. Furthermore, no single strategy appears to be dominant as theoptimal generation of t-way interaction test suite is considered NP hard problem. Motivatedby the abovementioned challenges, this paper proposes and implements a new strategy, calledGeneral Variable Strength (GVS). It is demonstrated that GVS, in some cases, produces betterresults than other competing strategies.