The Fork and Pull-Request model is widely used in collaborative development of open source software (OSS), fostering innovation through independent repository copies, but it can also lead to inefficiencies and fragmentation. A key underexplored aspect is the integration effectiveness—the degree to which distributed original commits across forks are effectively integrated back into the main repository. It plays a critical role in OSS project productivity but remains poorly understood. In response, we introduce convergence entropy, a novel metric that quantifies the integration effectiveness by measuring the similarity between distributions of original and merged commits across forks, adjusted for integration ratio. This metric highlights not only the volume of contributions but also their diversity and coordination, offering a unique lens to understand forking practices. Moreover, we explore the relationship between convergence entropy and three dimensions of OSS project productivity, showing significant correlations. We also observe that other factors can alter this dynamic.

Agents significantly enhance the capabilities of standalone Large Language Models (LLMs) by perceiving environments, making decisions, and executing actions. However, LLM agents still face challenges in tasks that require multiple decision-making steps. Estimating the value of actions in specific tasks is difficult when intermediate actions are neither appropriately rewarded nor penalized. In this paper, we propose leveraging a task-relevant Q-value model to guide action selection. Specifically, we first collect decision-making trajectories annotated with step-level Q values via Monte Carlo Tree Search (MCTS) and construct preference data. We then use another LLM to fit these preferences through step-level Direct Policy Optimization (DPO), which serves as the Q-value model. During inference, at each decision-making step, LLM agents select the action with the highest Q value before interacting with the environment. We apply our method to various open-source and API-based LLM agents, demonstrating that Q-value models significantly improve their performance. Notably, the performance of the agent built with Phi-3-mini-4k-instruct improved by 103% on WebShop and 75% on HotPotQA when enhanced with Q-value models, even surpassing GPT-4o-mini. Additionally, Q-value models offer several advantages, such as generalization to different LLM agents and seamless integration with existing prompting strategies.

Self-improving alignment leveraging large language models (LLMs) to automatically generate synthetic preference data has garnered significant attention as a means of reducing reliance on human labelers. These methods typically employ the LLM-as-a-judge mechanism, where the LLM generates responses and then employs itself to judge which response best aligns with the given prompt for curating the binary self-preferred dataset. However, these methods encounter two major challenges: (1) LLM-as-a-judge often produces error-prone evaluations, resulting in low-quality preference annotation, and (2) their optimization strategies often overlook the strength of preferences within binary pairs, leading to overfitting. This paper proposes a novel method, Preference-Strength-aware Optimization (PSO), to address these issues. Specifically, PSO frames the preference annotation process as a judgment token prediction task using the generative preference model to produce reliable judgments. The predicted judgment token indicates the preferred response and its corresponding probability reflects the disparity between responses, referred to as preference strength. Based on this strength, we introduce a new preference-strength-aware loss to adaptively reweight the impact of different response pairs on optimization, concentrating the model's learning on high-quality response pairs. Our experiments demonstrate that PSO significantly improves performance in preference benchmarks, achieving stronger alignment with human preferences, reducing verbose responses, and mitigating overfitting. Furthermore, PSO exhibits robust generalization and sample efficiency, offering a scalable and promising solution for LLM alignment without relying on human-annotated preferences.

Large Language Model (LLM) agents frequently produce sub-optimal actions when tackling complex, multi-step decision-making tasks. Employing self-critique to identify flaws and suggest enhancements is an effective strategy for refining actions. Although trajectory-level critique is commonly employed, it often fails to identify flawed steps accurately. In this paper, we introduce SLSC-MCTS, a method that integrates Monte Carlo Tree Search with Step-Level Self-Critique to enhance LLM agents during both testing and self-training phases. During decision tree expansion with SLSC-MCTS, the LLM agent initially generates an action, receives environmental feedback, and subsequently generates further actions via self-critique and refinement. Through multiple episodes of SLSC-MCTS, LLM agents can effectively utilize step-level critiques while disregarding ineffective ones based on node values, thereby incorporating the critiques more robustly. Additionally, our method further empowers LLM agents in a self-training manner, collecting training data from the constructed decision tree to iteratively fine-tune the LLM agents. The self-training data gathered via SLSC-MCTS is diverse and high-quality, which further enhances the reasoning, critiquing, and refining abilities of LLM agents. Experimental results demonstrate that SLSC-MCTS significantly improves LLM agents during testing, surpassing state-of-the-art baselines and achieving shorter task completion trajectories across information retrieval benchmarks such as WebShop and HotPotQA. After three iterations of self-training, LLM agents established by Llama-3.1-8B-Instruct show substantial improvement, even surpassing human experts in WebShop.

In the realm of open source software (OSS) development, the resolution of issues is not just a technical task but a pivotal activity that drives ongoing enhancement and secures a project's sustainability. Contributing to issues is the majority form for external contributors to take part in OSS projects. Although the significance of external contributors is recognized, their contributions in issue process still lacks full quantification and clarity, and the correlation between their contributions and the project productivity remains unclear. In response, we propose issue entropy, a novel metric that quantifies the complexity of event sequence in issue process applied to study the external contributions. The metric applies principles of information theory to scrutinize granular details within a project's issue-related activities, providing a unique lens to understand and assess external contributions. To explore the correlation between external contributions and project productivity, we employed issue entropy as a novel way to examine external contributions, and analyzed its correlation with new bugs, commits, and bug fix time, serving as proxies for OSS project productivity. Our findings reveal a strong positive correlation with new bugs, variable relationship with commit volume based on company sponsorship, and significant negative correlation with bug fix time. We also observe the significant interactions between external contributions and other factors, such as project age and the number of files. Moreover, issue entropy offers a new perspective on the health of the OSS project ecosystem, potentially supporting further research and practical applications.

The prediction task of social media popularity aims to automatically forecast the future popularity of the posts by leveraging vast amounts of social media data. This data encompasses diverse visual and textual content, including photos, categories, custom tags, temporal information, and geographical data. Existing methods have explored multiple feature types to enhance popularity prediction. Despite their success, visual and textual features-both crucial pieces of information-are often simply concatenated after extraction, ignoring the divergence between these two feature spaces. In this paper, we propose a method to project visual and language information into an aligned semantic representation, thereby uncovering intricate associations between these two modalities. Specifically, we leverage the BLIP-2 model to understand and generate visual description text that encapsulates the content of photos. Semantic embeddings are then extracted from all available visual and textual information. Additionally, we deeply exploit user-related behavior and characteristic information to extract features, uncovering hidden clues for post popularity prediction. Leveraging these improvements, we conduct extensive experiments to demonstrate the effectiveness of our proposed method.

With the vigorous development of open‐source software, a huge number of open‐source projects and open‐source codes have been accumulated in open‐source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open‐source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open‐source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software engineering empirical knowledge and a deep learning algorithm. The experimental results show that our method has good effects on code tag generation and code snippet retrieval.

GitHub enables developers to expediently contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously is able to enhance work efficiency. However, multi-discussing also relies on developers’ rationally allocating their focus, which may result in the different influence on the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question that can help developers understand the benefits and limitations of multi-discussing. Using quantitative and qualitative methods, this paper proposes a groundbreaking study of the impact of multi-discussing on issue resolution in GitHub. First, we collect and analyze data from 624 GitHub projects to explore how multi-discussing affects the overall issue resolution of the project. Further, we investigate how multi-discussing affects the resolution of a single issue. We find that multi-discussing is a common behavior in GitHub. Also, multi-discussing is connected to a shorter average issue resolution latency of the project. However, during a single issue resolution, more multi-discussing behaviors tend to bring longer issue resolution latency. We also conduct the qualitative analysis to explore the developers’ experiences and expectations of multi-discussing.

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness, a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called “RepoLike,” to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

Collaborative development communities and knowledge sharing communities are highly correlated and mutually complementary. The knowledge sharing between these two types of open source communities can be very beneficial to both of them. However, it is a great challenge to automate this process. Current studies mainly focus on knowledge acquisition in one type of community, and few of them have tackle this problem efficiently. In this paper we take Android Issue Tracker and Stack Overflow as a case to study the mutual knowledge sharing between them. We propose an automatic approach by integrating semantic similarity with temporal locality between Android issues and Stack Overflow posts based on the internal citation-graph to reveal the potential associations between them. Our approach explores the internal citations in communities for closely related posts or issues clustering, exploits the rich semantics in fine-grained information of issues and posts for associations building, and leverages the temporal correlations between issues and posts in-depth for associations ranking. Extensive experiments show that the precision of our approach reaches 62.51 percent for top 10 recommendations when recommending Stack Overflow posts to Android issues, and 66.83 percent in reverse.

Code review is an important process to reduce code defects and improve software quality. However, in social coding communities using the pull-based model, everyone can submit code changes, which increases the required code review efforts. Therefore, there is a great need of knowing the process of code review and analyzing the pre-existing reviewer recommendation algorithms. In this paper, we do an empirical study about the PRs and their reviewers in Rails project. Moreover, we reproduce a popular and effective IR-based code reviewer recommendation algorithm and validate it on our dataset which contains 16,049 PRs. We find that the inactive reviewers are very important to code reviewing process, however, the pre-existing method's recommendation result strongly depends on the activeness of reviewers.

The crowds' continuous participation and contribution are the key factors for the success of open source projects.However, among the massive competitors, it is difficult for a project to attract enough contributors by just passively waiting for enthusiasts to join in.Instead, it should actively seek gifted developers.Most of the current studies mainly focus on recommending experts inside a repository for some specific development tasks.In this paper, we propose a novel approach ConRec to recommend potential contributors across the entire open source community for given projects.It leverages the developers' historical activities in projects to analyze their technical interests and technical connections with others.Thereafter, it combines collaborative filtering algorithm with text matching algorithm to recommend proper developers.We conducted extensive experiments on 5,995 open source projects and 2,938,620 developers in GitHub.The results show that the proposed algorithm can recommend contributors to open source projects with the best performance of 63% in accuracy, and solve the cold start problem as well.

Social coding represents a new style of software production and creation paradigm, and demands for new technologies of software reuse. Many people searching for projects package, we can provide good reuse recommendation. In this paper, we focus on an interesting research topic of recommending software repositories to social developers, which is challenging because of two points: the first is how to get the interest contexts of developers; and the second is how to rank the repository candidates for recommendation properly. We propose RepoLike, a new approach for recommending repositories to developers by predicting their interests. RepoLike explores the developers' historical development activities and the social connections with other programmers, mines the technical features of repositories and the dependencies among them, and then combines both aspects to recommend most interesting and inspiring repositories to developers. The experiment results show that our approach can surprisingly recommend closely correlated repositories to developers, and the critical test results show that the recommendation performance is strongly impacted by the interest context model.

With the rapid development of open source software, various elements such as OSS, developers, users and online posts, across different communities and their interactions constitute a novel software ecosystem.Most of the current researches about software ecosystems care the connections between software, and few of them consider the relationship across communities from an overall perspective, and fail to cover the users and their activities which should be an indispensable part of the OSS ecosystem.This paper model the OSS ecosystem as a graph, which combines different types of OSS communities as a whole.Based on this graph model, we analyze the characteristics of ecosystem, like the evolution, competition and symbiosis.In addition, we build a recommendation system as well, and the experiment results suggest the validation of our approach.

The massive amounts of open source software provide sufficient reusable resources for software development. Most of the OSS communities adopt a kind of categorization or tagging mechanism to organize the software. However, the categorization often too coarse, while the tags are flat and fail to capture the inter-relation among them. In this paper, we propose a novel approach to reveal the latent relations between tags and build a tag hierarchy to help locate resources. We firstly build a co-occurrence network, based on which we compare the connotations of tags and construct a preliminary hierarchy. Then we leverage the domain knowledge of category in SourceForge to optimize and improve the relations between tags. At the end, we demonstrate the effectiveness of the constructed tag hierarchy with quantitative evaluation, which suggest the validation of our approach.

Issue resolution is a central task for software development. The efficiency of issue fixing largely relies on the issue report quality. Stack Overflow, which hosts rich and real-time posts about programming-specific problems, is a valuable external source of knowledge for issue resolution. In this paper, we present CrossLink, an analysis framework that automatically introduce related posts in Stack Overflow to issues in Android Issue Tracker. This helps developers to leverage the abundant and professional knowledge from massive programmers in Stack Overflow for issue resolution. CrossLink explores the semantic similarities as well as the temporal associations between the two types of repositories to recommend Stack Overflow posts to Android issues. The internal links in Stack Overflow are also employed to improve the linking accuracy. The experiments prove the effectiveness of CrossLink with precision of 62.51% for top-10 recommendations, which is significantly higher than the state-of-art method.