Mentorship in Open Source Software (OSS) projects is crucial for reducing barriers to entry for newcomers and for fostering the technical and social integration of new contributors. While mentorship in OSS has been recognized as essential for sustainable project growth, quantitative research supporting qualitative findings is not common. To address this gap, we present OpenMent, a comprehensive dataset comprising over 500,000 issue comments, pull request comments, and commit messages from GitHub projects participating in the Google Summer of Code (GSoC) program. OpenMent is curated to capture role-specific interactions and communication patterns between mentors and mentees, providing information on the challenges and dynamics of OSS mentoring. This dataset is designed to be a reusable resource for the Software Engineering community, enabling researchers and practitioners to explore mentorship dynamics and investigate the impact of mentoring on contributor retention. By making OpenMent openly available, we aim to facilitate future research in OSS mentorship, fostering a deeper understanding of mentorship challenges, strategies, and contributions to the growth and inclusivity of OSS ecosystems.

Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow or propose tree-based models that utilize Abstract Syntax Tree (AST), most PPLMs do not fully utilize the rich syntactical information in source code. Still, the input is considered a sequence of tokens. There are two issues; the first is computational inefficiency due to the quadratic relationship between input length and attention complexity. Second, any syntactical information, when needed as an extra input to the current PPLMs, requires the model to be pre-trained from scratch, wasting all the computational resources already used for pre-training the current models. In this work, we propose Named Entity Recognition (NER) adapters, lightweight modules that can be inserted into Transformer blocks to learn type information extracted from the AST. These adapters can be used with current PPLMs such as CodeBERT, GraphCodeBERT, and CodeT5. We train the NER adapters using a novel Token Type Classification objective function (TTC). We insert our proposed work in CodeBERT, building CodeBERTER, and evaluate the performance on two tasks of code refinement and code summarization. CodeBERTER improves the accuracy of code refinement from 16.4 to 17.8 while using 20% of training parameter budget compared to the fully fine-tuning approach, and the BLEU score of code summarization from 14.75 to 15.90 while reducing 77% of training parameters compared to the fully fine-tuning approach.

Context: Technical Debt (TD) is a metaphor used to describe code that is "not quite right." Although TD studies have gained momentum, TD has yet to be studied as thoroughly in non-Object-Oriented (OO) or scientific software such as R. R is a multi-paradigm programming language, whose popularity in data science and statistical applications has amplified in recent years. Due to R's inherent ability to expand through user-contributed packages, several community-led organizations were created to organize and peer-review packages in a concerted effort to increase their quality. Nonetheless, it is well-known that most R users do not have a technical programming background, being from multiple disciplines. Objective: The goal of this study is to investigate TD in the documentation of the peer-review of R packages led by rOpenSci. Method: We collected over 5,000 comments from 157 packages that had been reviewed and approved to be published at rOpenSci. We manually analyzed a sample dataset of these comments posted by package authors, editors of rOpenSci, and reviewers during the review process to investigate the types of TD present in these reviews. Results: The findings of our study include (i) a taxonomy of TD derived from our analysis of the peer-reviews (ii) documentation debt as being the most prevalent type of debt (iii) different user roles are concerned with different types of TD. For instance, reviewers tend to report some types of TD more than other roles, and the types of TD they report are different from those reported by the authors of a package. Conclusion: TD analysis in scientific software or peer-review is almost non-existent. Our study is a pioneer but within the context of R packages. However, our findings can serve as a starting point for replication studies, given our public datasets, to perform similar analyses in other scientific software or to investigate the rationale behind our findings.

The verification of Multiagent Systems (MAS) and Distributed Software Systems (DSS) has taken a special attention due to the growing demand of having DSS in recent years. The distributed functionality and lack of having a central control in MAS and DSS may cause to emerge new behaviors in the execution time. This unexpected behavior which was not seen in the requirements is known as emergent behavior and may cause irreparable damages. Detection of these emergent behaviors is more valuable and cost effective in the early phases compared to detecting them after the deployment. In this paper we propose a new technique for the detection of a specific type of emergent behavior in the design phase. We take the advantage of social network visualization in this method. The novelty and direct advantage of this technique is presenting the exact point and cause of emergent behavior.

Multi agent systems with autonomous interaction, negotiation and learning capabilities can efficiently model social behavior of individuals participating in a social network. A central problem in a social network is to identify the nodes that actively participate in the expansion of the net both physically and functionally. Several metrics have already been proposed to identify those hot spots. The algorithms to identify hot spots are either heuristic based or computationally expensive. In this paper we use an agent model of the social net and propose a method that can identify the neutral nodes, i.e. the nodes that can never be considered as hot spot nodes given the network topology and rules of negotiation among nodes. Therefore these nodes can be eliminated from the net. A direct advantage of this method is reducing the computational complexity for the configuration and identification of hot spots. Through a case study we have shown that the proposed method can lead to 33% reduction of computation regarding the number of agent types in the example.