Browsing by Author "WANG, Tao"

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    Automated Path Generation for Software Fault Localization
    (2005-06-15T06:31:20Z) WANG, Tao; ROYCHOUDHURY, Abhik
    Localizing the cause(s) of an observable error lies at the heart of program debugging. Fault localization often proceeds by comparing the failing program run with some ``successful'' run (a run which does not demonstrate the error). An issue here is to generate or choose a ``suitable'' successful run; this task is often left to the programmer. In this paper, we present an efficient technique where the construction of the successful run as well its comparison with the failing run is automated. Our method constructs a successful program run which is close to the failing run in terms of a distance metric capturing control flow. The distance metric takes into account the sequence of statements executed in the two runs, and not just the set of statements executed. We use the distance metric to locate ``similar'' branch instances which appear in the failing and successful run with different outcomes. The program statements for such branches are returned as bug report. In our experiments with the Siemens benchmark suite we found that the quality of our bug report compares well with those produced by existing fault localization approaches where the programmer manually provides or chooses a successful run.
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    Dynamic Slicing on Java Bytecode Traces
    (2007-03-19) WANG, Tao; ROYCHOUDHURY, Abhik
    Dynamic slicing is a well-known technique for program analysis, debugging and understanding. Given a program P and input I, it finds all program statements which directly/indirectly affect the values of some variables' occurrences when P is executed with I. In this paper, we develop a dynamic slicing method for sequential Java programs. Our technique proceeds by backwards traversal of the bytecode trace produced by an input I in a given program P. Since such traces can be huge, we use results from data compression to compactly represent bytecode traces. The major space savings in our method come from the optimized representation of (a) data addresses used as operands by memory reference bytecodes, and (b) instruction addresses used as operands by control transfer bytecodes. We show how dynamic slicing algorithms can directly traverse our compact bytecode traces without resorting to costly decompression. We also extend our dynamic slicing algorithm to perform ``relevant slicing''; th...
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    "UpSizeR: Synthetically Scaling an Empirical Relational Database"
    (2010-12-17) TAY, Y. C.; DAI, Bingtian; WANG, Tao; SUN, Yang; LIN, Yong; LIN, Yuting
    This paper presents UpSizeR, a software that takes as input an empirical relational dataset D and a scale factor s, and generates a synthetic dataset e D' that is similar to D but s times its size. Such a tool can be useful for scaling up D for scalability testing (s > 1), scaling down for application debugging (s < 1), or anonymization (s = 1). Experiments with Flickr show that query results and response times on UpSizeR output match those on crawled data. They also accurately predict throughput degradation for a scale out test.