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Crowdsourced testing is an emerging trend in software testing, which takes advantage of the efficiency of crowdsourced and cloud platforms. Crowdsourced testing has gradually been applied in many fields. In crowdsourced software testing, after the crowdsourced workers complete the test tasks, they submit the test results in test reports. Therefore, in crowdsourced software testing, checking a large number of test reports is an arduous but unavoidable software maintenance task. Crowdsourced test reports are numerous, complex, and need to be sorted to improve inspection efficiency. There are no systematic methods for prioritizing reports in crowdsourcing test report prioritization. However, in regression testing, test case prioritization technology has matured. Therefore, we migrate the test case prioritization method to crowdsourced test report prioritization and evaluate the effectiveness of these methods. We use natural language processing technology and word segmentation to process the text in the test reports. Then we use four methods to prioritize the reports: total greedy algorithm, additional greedy algorithm, genetic algorithm, and ART. The results show that these methods all perform well in prioritizing crowdsourced test reports, with an average APFD of more than 0.8.

Aerospace control software is the most important part of aerospace software. Since its potential defects endanger life and safety, there are strict requirements on product quality. Therefore, efficient and reliable software testing is essential. The traditional testing method has been challenging to meet its development requirements, and software automation testing has gradually become the main tool for testing aerospace control software. For the automation testing of aerospace control software, the core problem is to locate the GUI widgets on the software screenshots and identify their intent, which directly affects the accuracy of the test. Because of this, we use the widget recognition technology based on image matching and use the image understanding and analysis technology to extract the widget image in the screenshots. After obtaining the widget image, we use a convolutional neural network to extract image features and use the encoder module to encode the extracted information features as a tensor. The decoder module generates a word sequence conditional on tensor and previous output based on the encoded information. We also conduct an empirical study to evaluate the accuracy of widget recognition and intention generation. For widget recognition, our average IoU reached 0.81. For widget intent generation, our model BLEU-1 is 0.567, BLEU-2 is 0.356, BLEU-3 is 0.261, BLEU-4 is 0.131. The results show that our method is very effective.