When encountering unfamiliar APIs, developers tend to seek help from API tutorials and Stack Overflow (SO). API tutorials help developers understand the API knowledge in a general context, while SO often explains the API knowledge in a specific programming task. Thus, tutorials and SO posts together can provide more API knowledge. However, it is non-trivial to retrieve API knowledge from both API tutorials and SO posts based on natural language queries. Two major problems are irrelevant API knowledge in two different resources and the lexical gap between the queries and documents. In this article, we regard a fragment in tutorials and a Question and Answering (Q&A) pair in SO as a knowledge item (KI). We generate ⟨ API, FRA ⟩ pairs (FRA stands for fragment) from tutorial fragments and APIs and build ⟨ API, QA ⟩ pairs based on heuristic rules of SO posts. We fuse ⟨ API, FRA ⟩ pairs and ⟨ API, QA ⟩ pairs to generate API knowledge (AK for short) datasets, where each data item is an ⟨ API, KI ⟩ pair. We propose a novel approach, called PLAN, to automatically retrieve API knowledge from both API tutorials and SO posts based on natural language queries. PLAN contains three main stages: (1) API knowledge modeling, (2) query mapping, and (3) API knowledge retrieving. It first utilizes a deep-transfer-metric-learning-based relevance identification (DTML) model to effectively find relevant ⟨ API, KI ⟩ pairs containing two different knowledge items (⟨ API, QA ⟩ pairs and ⟨ API, FRA ⟩ pairs) simultaneously. Then, PLAN generates several potential APIs as a way to reduce the lexical gap between the query and ⟨ API, KI ⟩ pairs. According to potential APIs, we can select relevant ⟨ API, KI ⟩ pairs to generate potential results. Finally, PLAN returns a list of ranked ⟨ API, KI ⟩ pairs that are related to the query. We evaluate the effectiveness of PLAN with 270 queries on Java and Android AK datasets containing 10,072 ⟨ API, KI ⟩ pairs. Our experimental results show that PLAN is effective and outperforms the state-of-the-art approaches. Our user study further confirms the effectiveness of PLAN in locating useful API knowledge.

Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging. Besides, most existing CPDP methods usually require sufficient and labeled data. However, acquiring lots of labeled data for a new project is difficult while obtaining the unlabeled data is relatively easy. A desirable approach is building a prediction model on unlabeled data and labeled data. CPDP in this scenario is called cross-project semi-supervised defect prediction (CSDP). Recently, generative adversarial networks have achieved impressive results with these strong ability of learning data distribution and discriminative representation. For effectively learning the discriminative features of data from different projects, we propose a Discriminative Adversarial Feature Learning (DAFL) approach for CSDP. DAFL consists of feature transformer and project discriminator, which compete with each other. A feature transformer tries to generate feature representation, which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data. A project discriminator tries to discriminate source and target instances on the generated representation. Experiments on 16 projects show that DAFL performs significantly better than baselines.