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Executing large test suites is time and resource consuming, sometimesimpossible, and such test suites typically contain many redundant test cases.Hence, test case minimization is used to remove redundant test cases that areunlikely to detect new faults. However, most test case (suite) minimizationtechniques rely on code coverage (white-box), model-based features, orrequirements specifications, which are not always accessible by test engineers.Recently, a set of novel techniques was proposed, called FAST-R, relying solelyon test case code for test case minimization, which appeared to be much moreefficient than white-box techniques. However, it achieved a comparable lowfault detection capability for Java projects, making its applicationchallenging in practice. This paper proposes ATM (AST-based Test caseMinimizer), a similarity-based, search-based test case minimization technique,taking a specific budget as input, that also relies exclusively on the sourcecode of test cases but attempts to achieve higher fault detection throughfiner-grained similarity analysis and a dedicated search algorithm. ATMtransforms test case code into Abstract Syntax Trees (AST) and relies on fourtree-based similarity measures to apply evolutionary search, specificallygenetic algorithms, to minimize test cases. We evaluated the effectiveness andefficiency of ATM on a large dataset of 16 Java projects with 661 faultyversions using three budgets ranging from 25% to 75% of test suites. ATMachieved significantly higher fault detection rates (0.82 on average), comparedto FAST-R (0.61 on average) and random minimization (0.52 on average), whenrunning only 50% of the test cases, within practically acceptable time (1.1-4.3hours, on average), given that minimization is only occasionally applied whenmany new test cases are created (major releases). Results achieved for otherbudgets were consistent.

ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search