Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP.

In wireless sensor networks (WSNs), the widely distributed sensors make the real-time processing of data face severe challenges, which prompts the use of edge computing. However, some problems that occur during the operation of sensors will cause unreliability of the collected data, which can result in inaccurate results of edge computing-based processing; thus, it is necessary to detect potential abnormal data (also known as outliers) in the sensor data to ensure their quality. Although the clustering-based outlier detection approaches can detect outliers from the static data, the feature of streaming sensor data requires the detection operation in a one-pass fashion; in addition, the clustering-based approaches also do not consider the time correlation among the streaming sensor data, which leads to its low detection accuracy. To solve these problems, we propose an efficient outlier detection approach based on neighbor difference and clustering, namely, ODNDC, which not only quickly and accurately detects outliers but also identifies the source of outliers in the streaming sensor data. Experiments on a synthetic dataset and a real dataset show that the proposed ODNDC approach achieves great performance in detecting outliers and identifying their sources, as well as the low time consumption.

SummaryCombinatorial testing (CT) is considered as a practical approach to detect software faults, which has arisen from the interaction between factors affecting the software behavior. However, most of the traditional algorithms on CT generation did not take advantage of the execution results of the earlier test cases, as well as neglect the impact of the nonequilibrium input parameter model (NE‐IPM) effect on redundant test cases, which bring a deleterious effect to the detection accuracy of the software faults. To solve these problems, we propose a novel CT approach with fuzzing strategy called CTAF. Based on the idea that fuzzing is performed during execution, CTAF exploits the execution results of earlier tests to provide guidance for subsequent test generation thereby reducing the redundant test cases without compromising the diversity of test cases. And then, we designed three experiments on real subjects of six open source software systems, and the experimental results show that the proposed CTAF approach can effectively improve the NE‐IPM effect and enhance the detection accuracy of software faults.

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