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During software development, programmers often rely on a wide range of application programming interfaces (APIs) to facilitate their tasks. However, APIs have been growing rapidly in recent years, making it difficult for developers to choose among the many APIs that suit their programming needs. To facilitate the development process, automatic API recommendation is becoming increasingly important. Although there have been many effective research methods, these methods have a high dependence on the accuracy of the user's description of his own task, and there is a knowledge difference between the user's query and the user's actual task, increasing the difficulty of accurate API recommendation. In this article, we propose REAPI, a method to bridge the knowledge gap between the user's query and the user's actual task to improve the recommendation accuracy. The REAPI approach involves reconstructing query by tapping into Stack Overflow data to glean user intentions. Refactoring the user's query to display implicit information can better capture the user's true intentions. Specifically, we generate three candidate reconstruction statements based on natural language queries and Stack Overflow data and incorporate user feedback to refine and select the final statement. To evaluate the effectiveness of REAPI, we conducted experiments at both the class-level and method-level. Our results show that REAPI outperforms state-of-the-art baselines across key evaluation metrics such as S@1, S@3, S@10, MRR, and MAP.

App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning; however, most of the studies strongly rely on predefined classes or manually annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously. In the article, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: the review helpfulness prediction module, topic-sentiment modeling module, and multifactor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multifactor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.

Adapting deep learning (DL) techniques to automate nontrivial coding activities, such as code documentation and defect detection, has been intensively studied recently. Learning to predict code changes is one of the popular and essential investigations. Prior studies have shown that DL techniques, such as neural machine translation (NMT), can benefit meaningful code changes, including bug fixing and code refactoring. However, NMT models may encounter bottleneck when modeling long sequences; thus, they are limited in accurately predicting code changes. In this article, we design a Transformer-based approach, considering that the Transformer has proven effective in capturing long-term dependencies. Specifically, we propose a novel model named DTrans. For better incorporating the local structure of code, i.e., statement-level information in this article, DTrans is designed with dynamically relative position encoding in the multihead attention of the Transformer. Experiments on benchmark datasets demonstrate that DTrans can more accurately generate patches than the state-of-the-art methods, increasing the performance by at least 5.45–46.57% in terms of the exact match metric on different datasets. Moreover, DTrans can locate the lines to change with 1.75–24.21% higher accuracy than the existing methods.