Improved Modified Condition/ Decision Coverage using Code Transformation Techniques

Modified Condition / Decision Coverage (MC / DC) is a white box testing criteria aiming to prove that all conditions involved in a predicate can influence the predicate value in the desired way. In regulated domains such as aerospace and safety critical domains, software quality assurance is subjected to strict regulations such as the DO-178B standard. Though MC/DC is a standard coverage criterion, existing automated test data generation approaches like CONCOLIC testing do not support MC/DC. To address this issue we present an automated approach to generate test data that helps to achieve an increase in MC/DC coverage of a program under test. We use code transformation techniques for transforming program. This transformed program is inserted into the CREST TOOL. It drives CREST TOOL to generate test suite and increase the MC/DC coverage. Our tech-nique helps to achieve a signicant increase in MC/DC coverage as compared to traditional CONCOLIC testings. Our experimental results show that the proposed approach helps to achieve on the average approximately 20.194 % for Program Code Transformer(PCT) and 25.447 % for Exclusive-Nor Code Transformer. The average time taken for seventeen programs is 6.89950 seconds

The Program Decision Logic Approach to Predicated Execution

Modern compilers must expose sufficient amounts of Instruction-Level Parallelism (ILP) to achieve the promised performance increases of superscalar and VLIW processors. One of the major impediments to achieving this goal has been inefficient programmatic control flow. Historically, the compiler has translated the programmer's original control structure directly into assembly code with conditional branch instructions. Eliminating inefficiencies in handling branch instructions and exploiting ILP has been the subject of much research. However, traditional branch handling techniques cannot significantly alter the program's inherent control structure. The advent of predication as a program control representation has enabled compilers to manipulate control in a form more closely related to the underlying program logic. This work takes full advantage of the predication paradigm by abstracting the program control flow into a logical form referred to as a program decision logic network. This network is modeled as a Boolean equation and minimized using modified versions of logic synthesis techniques. After minimization, the more efficient version of the program's original control flow is re-expressed in predicated code. Furthermore, this paper proposes extensions to the HPL PlayDoh predication model in support of more effective predicate decision logic network minimization. Finally, this paper shows the ability of the mechanisms presented to overcome limits on ILP previously imposed by rigid program control structure