A Safe , E cient Regression Test Selection

نویسندگان

  • Gregg Rothermel
  • Mary Jean Harrold
چکیده

Regression testing is an expensive but necessary maintenance activity performed on modiied software to provide conndence that changes are correct and do not adversely aaect other portions of the software. A regression test selection technique chooses, from an existing test set, tests that are deemed necessary to validate modiied software. We present a new technique for regression test selection. Our algorithms construct control ow graphs for a procedure or program and its modiied version, and use these graphs to select tests that execute changed code from the original test suite. We prove that under certain conditions, the set of tests our technique selects includes every test from the original test suite that can expose faults in the modiied procedure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at least as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modiications. We have implemented our algorithms; initial empirical studies indicate that our technique can signiicantly reduce the cost of regression testing modiied software.

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تاریخ انتشار 1997