Semi-Proving: An Integrated Method for Program Proving, Testing, and Debugging

Abstract

Abstract-We present an integrated method for program proving, testing, and debugging. Using the concept of metamorphic relations, we select necessary properties for target programs. For programs where global symbolic evaluation can be conducted and the constraint expressions involved can be solved, we can either prove that these necessary conditions for program correctness are satisfied or identify all inputs that violate the conditions. For other programs, our method can be converted into a symbolic-testing approach. Our method extrapolates from the correctness of a program for tested inputs to the correctness of the program for related untested inputs. The method supports automatic debugging through the identification of constraint expressions that reveal failures.

A mapping study on testing non-testable systems

“…Semi-Proving attempts to use symbolic execution to enable such a verdict to generalise to all inputs (Chen et al 2011b).…”

“…Since all concrete executions represented by a symbolic execution are accounted for, it is possible to prove the correctness for the entire input domain, with respect to a certain property (Chen et al 2011b). However, this might not always be feasible.…”

“…For example, in some systems, certain loops, arrays, or pointers could cause such a large number of potential paths to exist, that it would be infeasible for Semi-Proving to check them all exhaustively (Chen et al 2011b). To alleviate this problem, one could restrict the application of the technique to specific program paths, replace some symbolic inputs with concrete values, use summaries of some of the SUT's functions instead of the functions themselves, or restrict the technique with upper-bounds (Chen et al 2011b). Chen et al (2011b) realised that the correctness of some symbolic test cases can be inferred from others.…”

“…To alleviate this problem, one could restrict the application of the technique to specific program paths, replace some symbolic inputs with concrete values, use summaries of some of the SUT's functions instead of the functions themselves, or restrict the technique with upper-bounds (Chen et al 2011b). Chen et al (2011b) realised that the correctness of some symbolic test cases can be inferred from others. For example, consider the max function and the following two symbolic test cases: max(x, y) and max (y, x).…”

“…Obtaining such coverage can improve the fault detection effectiveness of MT (Chen et al 2011b). Improvements in effectiveness for subtle faults, e.g.…”