A slicing-based approach for locating type errors

The effectiveness of a type checking tool strongly depends on the accuracy of the positional information that is associated with type errors. We present an approach where the location associated with an error message e is defined as a slice P_e of the program P being type checked. We show that this approach yields highly accurate positional information: P_e is a program that contains precisely those program constructs in P that caused error e. Semantically, we have the interesting property that type checking P_e is guaranteed to produce the same error e. Our approach is completely language-independent, and has been implemented for a significant subset of Pascal

A slicing-based approach for locating type errors

The effectiveness of a type checking tool strongly depends on the accuracy of the positional information that is associated with type errors. We present an approach where the location associated with an error message e is defined as a slice P_e of the program P being type checked. We show that this approach yields highly accurate positional information: P_e is a program that contains precisely those program constructs in P that caused error e. Semantically, we have the interesting property that type checking P_e is guaranteed to produce the same error e. Our approach is completely language-independent, and has been implemented for a significant subset of Pascal

Locating Faults with Program Slicing: An Empirical Analysis

Statistical fault localization is an easily deployed technique for quickly determining candidates for faulty code locations. If a human programmer has to search the fault beyond the top candidate locations, though, more traditional techniques of following dependencies along dynamic slices may be better suited. In a large study of 457 bugs (369 single faults and 88 multiple faults) in 46 open source C programs, we compare the effectiveness of statistical fault localization against dynamic slicing. For single faults, we find that dynamic slicing was eight percentage points more effective than the best performing statistical debugging formula; for 66% of the bugs, dynamic slicing finds the fault earlier than the best performing statistical debugging formula. In our evaluation, dynamic slicing is more effective for programs with single fault, but statistical debugging performs better on multiple faults. Best results, however, are obtained by a hybrid approach: If programmers first examine at most the top five most suspicious locations from statistical debugging, and then switch to dynamic slices, on average, they will need to examine 15% (30 lines) of the code. These findings hold for 18 most effective statistical debugging formulas and our results are independent of the number of faults (i.e. single or multiple faults) and error type (i.e. artificial or real errors)

Locating Boosted Kerr and Schwarzschild Apparent Horizons

We describe a finite-difference method for locating apparent horizons and illustrate its capabilities on boosted Kerr and Schwarzschild black holes. Our model spacetime is given by the Kerr-Schild metric. We apply a Lorentz boost to this spacetime metric and then carry out a 3+1 decomposition. The result is a slicing of Kerr/Schwarzschild in which the black hole is propagated and Lorentz contracted. We show that our method can locate distorted apparent horizons efficiently and accurately.Comment: Submitted to Physical Review D. 12 pages and 22 figure