In recent times, approximate computing is being looked at as a viable alternative for reduc- ing the energy consumption of programs, while marginally compromising on the correctness of their computation. The idea behi...
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In recent times, approximate computing is being looked at as a viable alternative for reduc- ing the energy consumption of programs, while marginally compromising on the correctness of their computation. The idea behind approximate computing is to introduce approxima- tions at various levels of the execution stack, with an attempt to realize the resource hungry computations on low resource consuming approximate hardware blocks. Approximate com- puting for program transformation faces a serious challenge of automatically identifying core program areas/statements where approximations can be introduced, with a quantifi- able measure of the resulting program correctness compromise. Introducing approximations randomly can cause performance deterioration without much energy advantage, which is undesirable. In this thesis, we introduce a verification-guided method to automatically iden- tify program blocks which lend themselves to easy approximations, while not compromising significantly on program correctness. Our method is based on identifying regions of code which are less influential for the computation of the program outputs and therefore, can be compromised with, however still having a potential of significant resource reduction. We take the help of assertions to quantify the effect of the resulting transformations on program outputs. We show experimental results to support our proposal.
The control flow of programs can be represented by directed graphs. In this paper we provide a uniform and detailed formal basis for control flow graphs combining known definitions and results with new aspects. Two gr...
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The control flow of programs can be represented by directed graphs. In this paper we provide a uniform and detailed formal basis for control flow graphs combining known definitions and results with new aspects. Two graph reductions are defined using only syntactical information about the graphs, but no semantical information about the represented programs. We prove some properties of reduced graphs and also about the paths in reduced graphs. Based on graphs, we define statement coverage and branch coverage such that coverage notions correspond to node coverage, and edge coverage, respectively.
As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alterna...
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As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alternatively the priority of validation suites after identifying the difficulties in the regression testing process of any product. The test case prioritisation technique is presented as a solution to this problem. It increases the fault detection rate. Earlier research studies have been implemented many techniques, but the rate of fault detection is not up to the mark. To overcome this drawback, we proposed HOTCP (Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints) model, which includes two steps: first is test case generation and the next one is test case prioritisation. The test case is generated from the released software. Consequently, test case prioritisation will be done by the optimisation strategy, in which the multi-objective function will be defined based on the constraints like statement coverage, branch coverage, contribution index and fault exposing potential. For this optimisation process, a new algorithm is proposed termed as CCCOA (Customised Coot and Chimp Optimisation Algorithm). The COOT optimisation and Chimp optimisation algorithms are combined in this algorithm. The system produces prioritised test cases, and the performance of the proposed method is validated in comparison to the traditional methods in terms of several metrics.
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