Enhanced Crowdsourced Test Report Prioritization via Image-and-Text Semantic Understanding and Feature Integration

Crowdsourced testing has gained prominence in the field of software testing due to its ability to effectively address the challenges posed by the fragmentation problem in mobile app testing. The inherent openness of crowdsourced testing brings diversity to the testing outcome. However, it also presents challenges for app developers in inspecting a substantial quantity of test reports. To help app developers inspect the bugs in crowdsourced test reports as early as possible, crowdsourced test report prioritization has emerged as an effective technology by establishing a systematic optimal report inspecting sequence. Nevertheless, crowdsourced test reports consist of app screenshots and textual descriptions, but current prioritization approaches mostly rely on textual descriptions, and some may add vectorized image features at the image-as-a-whole level or widget level. They still lack precision in accurately characterizing the distinctive features of crowdsourced test reports. In terms of prioritization strategy, prevailing approaches adopt simple prioritization based on features combined merely using weighted coefficients, without adequately considering the semantics, which may result in biased and ineffective outcomes. In this paper, we propose EncrePrior, an enhanced crowdsourced test report prioritization approach via image-and-text semantic understanding and feature integration. EncrePrior extracts distinctive features from crowdsourced test reports. For app screenshots, EncrePrior considers the structure (i.e., GUI layout) and the contents (i.e., GUI widgets), viewing the app screenshot from the macroscopic and microscopic perspectives, respectively. For textual descriptions, EncrePrior considers the Bug Description and Reproduction Step as the bug context. During the prioritization, we do not directly merge the features with weights to guide the prioritization. Instead, in order to comprehensively consider the semantics, we adopt a prioritize-reprioritize strategy. This practice combines different features together by considering their individual ranks. The reports are first prioritized on four features separately. Then, the ranks on four sequences are used to lexicographically reprioritize the test reports with an integration of features from app screenshots and textual descriptions. Results of an empirical study show that EncrePrior outperforms the representative baseline approach DeepPrior by 15.61% on average, ranging from 2.99% to 63.64% on different apps, and the novelly proposed features and prioritization strategy all contribute to the excellent performance of EncrePrior.