Empirical Software Engineering最新文献

This paper aims to improve software fault-proneness prediction by investigating the unexplored effects on classification performance of the temporal decisions made by practitioners and researchers regarding (i) the interval for which they will collect longitudinal features (software metrics data), and (ii) the interval for which they will predict software bugs (the target variable). We call these specifics of the data used for training and of the target variable being predicted the learning approach, and explore the impact of the two most common learning approaches on the performance of software fault-proneness prediction, both within a single release of a software product and across releases. The paper presents empirical results from a study based on data extracted from 64 releases of twelve open-source projects. Results show that the learning approach has a substantial, and typically unacknowledged, impact on classification performance. Specifically, we show that one learning approach leads to significantly better performance than the other, both within-release and across-releases. Furthermore, this paper uncovers that, for within-release predictions, the difference in classification performance is due to different levels of class imbalance in the two learning approaches. Our findings show that improved specification of the learning approach is essential to understanding and explaining the performance of fault-proneness prediction models, as well as to avoiding misleading comparisons among them. The paper concludes with some practical recommendations and research directions based on our findings toward improved software fault-proneness prediction.

The COVID-19 pandemic changed the way we live, work and the way we conduct research. With the restrictions of lockdowns and social distancing, various impacts were experienced by many software engineering researchers, especially whose studies depend on human participants. We conducted a mixed methods study to understand the extent of this impact. Through a detailed survey with 89 software engineering researchers working with human participants around the world and a further nine follow-up interviews, we identified the key challenges faced, the adaptations made, and the surprising fringe benefits of conducting research involving human participants during the pandemic. Our findings also revealed that in retrospect, many researchers did not wish to revert to the old ways of conducting human-orienfted research. Based on our analysis and insights, we share recommendations on how to conduct remote studies with human participants effectively in an increasingly hybrid world when face-to-face engagement is not possible or where remote participation is preferred.

With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts and find important information. Tags provide a concise feature dimension for users to locate their interested posts and for search engines to index the most relevant posts according to the queries. Most tags are only focused on the technical perspective (e.g., program language, platform, tool). In most cases, forum posts in online developer communities reveal the author’s intentions to solve a problem, ask for advice, share information, etc. The modeling of the intentions of posts can provide an extra dimension to the current tag taxonomy. By referencing previous studies and learning from industrial perspectives, we create a refined taxonomy for the intentions of technical forum posts. Through manual labeling and analysis on a sampled post dataset extracted from online forums, we understand the relevance between the constitution of posts (code, error messages) and their intentions. Furthermore, inspired by our manual study, we design a pre-trained transformer-based model to automatically predict post intentions. The best variant of our intention prediction framework, which achieves a Micro F1-score of 0.589, Top 1-3 accuracy of 62.6% to 87.8%, and an average AUC of 0.787, outperforms the state-of-the-art baseline approach. Our characterization and automated classification of forum posts regarding their intentions may help forum maintainers or third-party tool developers improve the organization and retrieval of posts on technical forums.

Developers rely on software ecosystems such as Maven to manage and reuse external libraries (i.e., dependencies). Due to the complexity of the used dependencies, developers may face challenges in choosing which library to use and whether they should upgrade or downgrade a library. One important factor that affects this decision is the number of potential vulnerabilities in a library and its dependencies. Therefore, state-of-the-art platforms such as Maven Repository (MVN) and Open Source Insights (OSI) help developers in making such a decision by presenting vulnerability information associated with every dependency. In this paper, we first conduct an empirical study to understand how the two platforms, MVN and OSI, present and categorize vulnerability information. We found that these two platforms may either overestimate or underestimate the number of associated vulnerabilities in a dependency, and they lack prioritization mechanisms on which dependencies are more likely to cause an issue. Hence, we propose a tool named VulNet to address the limitations we found in MVN and OSI. Through an evaluation of 19,886 versions of the top 200 popular libraries, we find VulNet includes 90.5% and 65.8% of the dependencies that were omitted by MVN and OSI, respectively. VulNet also helps reduce 27% of potentially unreachable or less impactful vulnerabilities listed by OSI in test dependencies. Finally, our user study with 24 participants gave VulNet an average rating of 4.5/5 in presenting and prioritizing vulnerable dependencies, compared to 2.83 (MVN) and 3.14 (OSI).

The traditional path to a software engineering career usually involves a post-secondary diploma in Software Engineering, Computer Science, or a related field. However, many individuals working as software engineers take a non-traditional path to their careers, starting from other industries or fields of study. This paper explores the barriers that individuals with non-traditional educational and occupational backgrounds face when pursuing a software engineering career and potential strategies to overcome those barriers. A two-stage methodology was used, consisting of an exploratory study followed by a follow-up survey. The exploratory study consisted of a grounded-theory-based qualitative analysis of relevant Reddit data to yield a framework around the barriers and possible mitigation strategies. These findings were then supplemented through a follow-up survey. Understanding these barriers and what strategies could be effective is an important step towards making software engineering more accessible to individuals with non-traditional backgrounds. In addition to fostering functional diversity, this might also serve to tackle labor shortages within the software engineering industry.

Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information (e.g., architectural models) to support architecting activities, such as architecture understanding, has received significant attention in recent years. However, there is a lack of clarity on what literature on mining architectural information is available. Consequently, this may create difficulty for practitioners to understand and adopt the state-of-the-art research results, such as what approaches should be adopted to mine what architectural information in order to support architecting activities. It also hinders researchers from being aware of the challenges and remedies for the identified research gaps. We aim to identify, analyze, and synthesize the literature on mining architectural information in software repositories in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. A Systematic Mapping Study (SMS) has been conducted on the literature published between January 2006 and December 2022. Of the 104 primary studies finally selected, 7 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 11 categories of sources have been leveraged for mining architectural information, among which version control system (e.g., GitHub) is the most popular source; 11 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 95 approaches and 56 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. This SMS provides researchers with promising future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.

The proxy pattern is a well-known design pattern with numerous use cases in several sectors of the software industry (e.g., network applications, microservices, and IoT). As such, the use of the proxy pattern is also a common approach in the development of complex decentralized applications (DApps) on the Ethereum blockchain. A contract that implements the proxy pattern (proxy contract) acts as a layer between the clients and the target contract, enabling greater flexibility (e.g., data validation checks) and upgradeability (e.g., online smart contract replacement with zero downtime) in DApp development. Despite the importance of proxy contracts, little is known about (i) how their prevalence changed over time, (ii) the ways in which developers integrate proxies in the design of DApps, and (iii) what proxy types are being most commonly leveraged by developers. In this paper, we present a large-scale exploratory study on the use of the proxy pattern in Ethereum. We analyze a dataset of all Ethereum smart contracts as of Sep. 2022 containing 50M smart contracts and 1.6B transactions, and apply both quantitative and qualitative methods in order to (i) determine the prevalence of proxy contracts, (ii) understand the ways they are deployed and integrated into applications, and (iii) uncover the prevalence of different types of proxy contracts. Our findings reveal that 14.2% of all deployed smart contracts are proxy contracts. We show that proxy contracts are being more actively used than non-proxy contracts. Also, the usage of proxy contracts in various contexts, transactions involving proxy contracts, and adoption of proxy contracts by users have shown an upward trend over time, peaking at the end of our study period. They are either deployed through off-chain scripts or on-chain factory contracts, with the former and latter being employed in 39.1% and 60.9% of identified usage contexts in turn. We found that while the majority (67.8%) of proxies act as an interceptor, 32.2% enables upgradeability. Proxy contracts are typically (79%) implemented based on known reference implementations with 29.4% being of type ERC-1167, a class of proxies that aims to cheaply reuse and clone contracts’ functionality. Our evaluation shows that our proposed behavioral proxy detection method has a precision and recall of 100% in detecting active proxies. Finally, we derive a set of practical recommendations for developers and introduce open research questions to guide future research on the topic.

Cross-Project Defect Prediction (CPDP) is a promising research field that focuses on detecting defects in projects with limited labeled data by utilizing prediction models trained on projects with abundant data. However, previous CPDP approaches based on Abstract Syntax Tree (AST) have often encountered challenges in effectively acquiring semantic and syntactic information, resulting in their limited ability to combine both productively. This issue arises primarily from the practice of flattening the AST into a linear sequence in many AST-based methods, leading to the loss of hierarchical syntactic structure and structural information within the code. Besides, other AST-based methods use a recursive way to traverse the tree-structured AST, which is susceptible to gradient vanishing. To alleviate these concerns, we introduce a novel CPDP method named defect prediction via Semantic and Syntactic Encoding (SSE) that enhances Zhang’s approach by encoding semantic and syntactic information while retaining and considering AST structure. Specifically, we perform pre-training on a large corpus using a language model to learn semantic information. Next, we present a new rule for splitting AST into subtrees to avoid vanishing gradients. Then, the absolute paths originating from the root node and leading to the leaf nodes are encoded as hierarchical syntactic information. Finally, we design an encoder to integrate syntactic information into semantic information and leverage Bi-directional Long-Short Term Memory to learn the entire tree representation for prediction. Experimental results on 12 benchmark projects illustrate that the SSE method we proposed surpasses current state-of-the-art methods.