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Ke Zhai , The University of Hong Kong, Hong Kong
Bo Jiang , The University of Hong Kong, Hong Kong
W.K. Chan , City University of Hong Kong, Hong Kong
ABSTRACT
Location-based services (LBS) are widely deployed. When the implementation of a LBS-enabled service has evolved, regression testing can be employed to assure the previously established behaviors not having been adversely affected. Proper test case prioritization helps reveal service anomalies efficiently. A key observation is that locations captured in the inputs and the expected outputs of test cases are physically correlated by the LBS-enabled service, and these services heuristically use estimated and imprecise locations for their computations, making these services tend to treat locations in close proximity homogenously. This paper exploits this observation. It proposes a suite of metrics and initializes them to demonstrate input-guided techniques and point-of-interest (POI) aware test case prioritization techniques, differing by whether the location information in the expected outputs of test cases is used. It reports a case study on a stateful LBS-enabled service. The case study shows that the POI-aware techniques can be more effective and more stable than the baseline, which reorders test cases randomly, and the input-guided techniques. We also find that one of the POI-aware techniques, cdist, is either the most effective or the second most effective technique among all the studied techniques in our evaluated aspects.
INDEX TERMS
ocation-based services, Regression testing, test case prioritization
CITATION
Ke Zhai, Bo Jiang, W.K. Chan, "Prioritizing Test Cases for Regression Testing of Location-based Services: Metrics, Techniques and Case Study", IEEE Transactions on Services Computing, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TSC.2012.40
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