The success of prompt learning when adapted to the fully qualified type name (FQN) inference has been demonstrated in the literature. However, the understanding of its success is limited in model outputs and model structures. In this article, we conduct a thorough study on the behaviors of prompt learning in FQN inference from the perspectives of sensitivity and usefulness. Rather than simply masking some knowledge, we first perform sensitivity analysis on five aspects to reveal how much FQN knowledge to include, how much to mask, and where to mask, and then yield an efficient configuration strategy. We further conduct a usefulness analysis in three aspects to demonstrate the superiority of the proposed configuration strategy. This suggests that the strong performance of our model is attributable to the homogeneity among large code pre-training, FQN prompt learning, and type inference as a fill-in-blank task. Finally, we summarize a practical guideline on best practices and pitfalls to avoid when applying prompt learning to FQN inference and other software engineering (SE) tasks.

Giant pre-trained code models (PCMs) start coming into the developers’ daily practices. Understanding the type and amount of software knowledge in PCMs is essential for integrating PCMs into software engineering (SE) tasks and unlocking their potential. In this work, we conduct the first systematic study on the SE factual knowledge in the state-of-the-art PCM CoPilot, focusing on APIs’ Fully Qualified Names (FQNs), the fundamental knowledge for effective code analysis, search and reuse. Driven by FQNs’ data distribution properties, we design a novel lightweight in-context learning on Copilot for FQN inference, which does not require code compilation as traditional methods or gradient update by recent FQN prompt-tuning. We systematically experiment with five in-context learning design factors to identify the best configuration for practical use. With this best configuration, we investigate the impact of example prompts and FQN data properties on CoPilot's FQN inference capability. Our results confirm that CoPilot stores diverse FQN knowledge and can be applied for FQN inference due to its high accuracy and non-reliance on code analysis. Additionally, our extended study shows that the in-context learning method can be generalized to retrieve other SE factual knowledge embedded in giant PCMs. Furthermore, we find that the advanced general model GPT-4 also stores substantial SE knowledge. Comparing FQN inference between CoPilot and GPT-4, we observe that as model capabilities improve, the same prompts yield better results. Based on our experience interacting with Copilot, we discuss various opportunities to improve human-CoPilot interaction in the FQN inference task.

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers may encounter unique problems when employing open-source AI repositories.This paper presents an empirical study that investigates the issues in the repositories of open-source AI repositories to assist developers in understanding problems during the process of employing AI systems. We collect 576 repositories from the PapersWithCode platform. Among these repositories, we find 24,953 issues by utilizing GitHub REST APIs. Our empirical study includes three phases. First, we manually analyze these issues to categorize the problems that developers are likely to encounter in open-source AI repositories. Specifically, we provide a taxonomy of 13 categories related to AI systems. The two most common issues are runtime errors (23.18%) and unclear instructions (19.53%). Second, we see that 67.5% of issues are closed. We also find that half of these issues resolve within four days. Moreover, issue management features, e.g., label and assign, are not widely adopted in open-source AI repositories. In particular, only 7.81% and 5.9% of repositories label issues and assign these issues to assignees, respectively. Finally, we empirically show that employing GitHub issue management features and writing issues with detailed descriptions facilitate the resolution of issues. Based on our findings, we make recommendations for developers to help better manage the issues of open-source AI repositories and improve their quality.

In this paper, we study the problem of part-of-speech (POS) tagging for security vulnerability descriptions (SVD). In contrast to newswire articles, SVD often contains a high-level natural language description of the text composed of mixed language studded with codes, domain-specific jargon, vague language, and abbreviations. Moreover, training data dedicated to security vulnerability research is not widely available. Existing neural network-based POS tagging has often relied on manually annotated training data or applying natural language processing (NLP) techniques, suffering from two significant drawbacks. The former is extremely time-consuming and requires labor-intensive feature engineering and expertise. The latter is inadequate to identify linguistically-informed words specific to the SVD domain. In this paper, we propose an automatic approach to assign POS tags to tokens in SVD. Our approach uses the character-level representation to automatically extract orthographic features and unsupervised word embeddings to capture meaningful syntactic and semantic regularities from SVD. The character level representations are then concatenated with the word embedding as a combined feature, which is then learned and used to predict the POS tagging. To deal with the issue of the poor availability of annotated security vulnerability data, we implement a finetuning approach. Our approach provides public access to a POS annotated corpus of ~8M tokens, which serves as a training dataset in this domain. Our evaluation results show a significant improvement in accuracy (17.72%-28.22%) of POS tagging in SVD over the current approaches.

Application programming interface (API) documentation well describes an API and how to use it. However, official documentation does not describe "how not to use it" or the different kinds of errors when an API is used wrongly. Programming caveats are negative usages of an API. When these caveats are overlooked, errors may emerge, leading to heavy discussions on Q&A websites like Stack Overflow. In this demonstration, we present API Caveat Explorer, a search system to explore API caveats that are mined from large-scale unstructured discussions on Stack Overflow. API Caveat Explorer takes API-oriented queries such as "HashMap" and retrieves API caveats by text summarization techniques. API caveats are represented by sentences, which are context-independent, prominent, semantically diverse and non-redundant. The system provides a web-based interface that allows users to interactively explore the full picture of all discovered caveats of an API, and the details of each. The potential users of API Caveat Explorer are programmers and educators for learning and teaching APIs.

Negative caveats of application programming interfaces (APIs) are about “how not to use an API,” which are often absent from the official API documentation. When these caveats are overlooked, programming errors may emerge from misusing APIs, leading to heavy discussions on Q&A websites like Stack Overflow. If the overlooked caveats could be mined from these discussions, they would be beneficial for programmers to avoid misuse of APIs. However, it is challenging because the discussions are informal, redundant, and diverse. For this, for example, we propose Disca, a novel approach for automatically Distilling desirable API negativecaveats from unstructured Q&A discussions. Through sentence selection and prominent term clustering, Discaensures that distilled caveats are context‐independent, prominent, semantically diverse, and nonredundant. Quantitative evaluation in our experiments shows that the proposed Discasignificantly outperforms four text‐summarization techniques. We also show that the distilled API negative caveats could greatly augment API documentation through qualitative analysis.

Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly self-reports. The monthly work reports in our study are from two IT companies. Monthly reports in these two companies are used to report a developer's activities and working hours in a month. We would like to investigate whether a developer will leave the company after he/she enters company for one year based on his/her first six monthly reports. To perform our prediction, we extract many factors from monthly reports, which are grouped into 6 dimensions. We apply several classifiers including naive Bayes, SVM, decision tree, kNN and random forest. We conduct an experiment on about 6-years monthly reports from two companies, this data contains 3,638 developers over time. We find that random forest classifier achieves the best performance with an F1-measure of 0.86 for retained developers and an F1-measure of 0.65 for not-retained developers. We also investigate the relationship between our proposed factors and developers' departure, and the important factors that indicate a developer's departure. We find the content of task report in monthly reports, the standard deviation of working hours, and the standard deviation of working hours of project members in the first month are the top three important factors.

In software development process, developers often seek solutions to the technical problems they encounter by searching relevant questions on Q&A sites. When developers fail to find solutions on Q&A sites in their native language (e.g., Chinese), they could translate their query and search on the Q&A sites in another language (e.g., English). However, developers who are non-native English speakers often are not comfortable to ask or search questions in English, as they do not know the proper translation of the Chinese technical words into the English technical words. Furthermore, the process of manually formulating cross-language queries and determining the weight of query words is a tedious and time-consuming process.