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(Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting developers to understand and maintain code. Inspired by neural machine translation, deep learning-based code summarization techniques widely adopt an encoder-decoder framework, where the encoder transforms given code snippets into context vectors, and the decoder decodes context vectors into summaries. Recently, large-scale pre-trained models for source code (e.g., CodeBERT and UniXcoder) are equipped with encoders capable of producing general context vectors and have achieved substantial improvements on the code summarization task. However, although they are usually trained mainly on code-focused tasks and can capture general code features, they still fall short in capturing specific features that need to be summarized. In a nutshell, they fail to learn the alignment between code snippets and summaries (code-summary alignment for short). In this paper, we propose a novel approach to improve code summarization based on summary-focused tasks. Specifically, we exploit a multi-task learning paradigm to train the encoder on three summary-focused tasks to enhance its ability to learn code-summary alignment, including unidirectional language modeling (ULM), masked language modeling (MLM), and action word prediction (AWP). Unlike pre-trained models that mainly predict masked tokens in code snippets, we design ULM and MLM to predict masked words in summaries. Intuitively, predicting words based on given code snippets would help learn the code-summary alignment. In addition, existing work shows that AWP affects the prediction of the entire summary. Therefore, we further introduce the domain-specific task AWP to enhance the ability of the encoder to learn the alignment between action words and code snippets. We evaluate the effectiveness of our approach, called Esale, by conducting extensive experiments on four datasets, including two widely used datasets JCSD and PCSD, a cross-project Java dataset CPJD, and a multilingual language dataset CodeSearchNet. Experimental results show that Esale significantly outperforms state-of-the-art baselines in all three widely used metrics, including BLEU, METEOR, and ROUGE-L. Moreover, the human evaluation proves that the summaries generated by Esale are more informative and closer to the ground-truth summaries.

The importance of mobile application (app) quality assurance is increasing with the rapid development of the mobile Internet. Automated test generation approaches, as a dominant direction of app quality assurance, follow specific models or strategies, targeting at optimizing the code coverage. Such approaches lead to a huge gap between testing execution and app business logic. Test scripts developed by human testers consider business logic by focusing on testing scenarios. Due to the GUI-intensive feature of mobile apps, human testers always understand app GUI to organize test scripts for scenarios. This inspires us to utilize domain knowledge from app GUI understanding for scenario-based test generation. In this paper, we propose a novel approach, ScenTest, for scenario-based mobile app testing with event knowledge graph (EKG) via GUI image understanding. ScenTest tries to start automated testing by imitating human practices and integrating domain knowledge into scenario-based mobile app testing, realizing fully automated testing on target testing scenarios for the first time. ScenTest extracts four kinds of entities and five kinds of corresponding relationships from crowdsourced test reports, where the test events and app GUI information are presented, and constructs the EKGs for specific scenarios. Then, ScenTest conducts test generation for specific scenarios on different apps with the guidance of EKG with the combination consideration of app current state and testing context. We conduct an evaluation on ScenTest on different aspects. The results show that the test generation of ScenTest on the basis of EKG is effective, and ScenTest reveals 150+ distinct real-world bugs in specific scenarios compared with representative baselines.

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22 k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.