Abstract. Software fault localization involves locating the exact cause of error for a "failing" execution run -a run which exhibits an unexpected behavior. Given such a failing run, fault localization often proceeds by comparing the failing run with a "successful" run, that is, a run which does not exhibit the unexpected behavior. One important issue here is the choice of the successful run for such a comparison. In this paper, we propose a control flow based difference metric for this purpose. The difference metric takes into account the sequence of statement instances (and not just the set of these instances) executed in the two runs, by locating branch instances with similar contexts but different outcomes in the failing and the successful runs. Given a failing run π f and a pool of successful runs S, we choose the successful run πs from S whose execution trace is closest to π f in terms of the difference metric. A bug report is then generated by returning the difference between π f and πs. We conduct detailed experiments to compare our approach with previously proposed difference metrics. In particular, we evaluate our approach in terms of (a) effectiveness of bug report for locating the bug, (b) size of bug report and (c) size of successful run pool required to make a decent choice of successful run.

Magneto-acousto-electrical tomography (MAET) is an imaging modality proposed to conduct noninvasive electrical conductivity imaging of biological tissue with high spatial resolution. In this study, we present a method of MAET in coil detection mode, which is named as magneto-acousto-electrical tomography with magnetic induction (MAET-MI). Based on the analysis of the mechanism of MAET-MI, we derive a reciprocal theorem and give an integral equation for computing the induced voltage of the coil. The forward problem of MAET-MI can be solved by this integral equation. In the inverse problem of MAET-MI, two steps are taken to reconstruct the conductivity. The first step is to reconstruct the curl of the eddy current density in the reciprocal process by the compression sensing method. And then the conductivity is recovered by the iterative methods such as the Levenberg-Marquardt algorithm. Both the mechanism of MAET-MI and the reconstruction of conductivity are verified by computer simulations. We have also conducted the phantom experiments. The reconstructed images are approximately consistent with the phantom's conductivity. The imaging results prove the ability and the reliability of our proposed methods. It is shown that the relative conductivity distribution can be reconstructed with our proposed reciprocal theorem in MAET-MI modality. Comparing with the traditional MAET, The MAET-MI modality would benefit from the noncontact measurement and be convenient for clinical application.

One of the important issues in constructing interprocedural program slices is maintaining context-sensitivity or preserving calling context when a procedure is called at multiple call sites. Though a number of context-sensitive t e c hniques have been presented in the last decade, the following important questions remain unanswered: 1) What is the level of precision lost if context-sensitivity is not maintained ? 2) What are the additional costs for achieving context-sensitivity ?In this paper, we e v aluate a PDG based explicitly contextsensitive interprocedural program slicing technique for accuracy and e ciency. We compare this technique against a context-insensitive technique using a program slicing framework we h a ve d e v eloped for Java programs for which only the byte-code sequences are available.Our results show that the context-sensitive t e c hnique, in spite of its worst case exponential complexity, can be very e cient in practice. The execution time for our set of benchmarks is, on the average, only twice as much as the execution time for the context-insensitive technique. The results on the accuracy for the context-insensitive t e c hnique are mixed. For 53% of the 2464 slicing criteria used in our experiments, the context-insensitive technique does not loose accuracy. However, in some cases, it can also lead to slices with 35 times more vertices. On the average, the slices constructed from the context-insensitive t e c hnique are twice as large as the one from the context-sensitive t e c hnique.

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