Empirical software engineering research often depends on datasets of code repository artifacts, where sampling strategies are employed to enable large-scale analyses. The design and evaluation of these strategies are critical, as they directly influence the generalizability of research findings. However, sampling remains an underestimated aspect in software engineering research: we identify two main challenges related to (1) the design and representativeness of sampling approaches, and (2) the ability to reason about the implications of sampling decisions on generalizability. To address these challenges, we propose a Domain-Specific Language (DSL) to explicitly describe complex sampling strategies through composable sampling operators. This formalism supports both the specification and the reasoning about the generalizability of results based on the applied sampling strategies. We implement the DSL as a Python-based fluent API, and demonstrate how it facilitates representativeness reasoning using statistical indicators extracted from sampling workflows. We validate our approach through a case study of MSR papers involving code repository sampling. Our results show that the DSL can model the sampling strategies reported in recent literature.

Code review is a crucial but often complex, subjective, and time-consuming activity in software development. Over the past decades, significant efforts have been made to automate this process. Early approaches focused on knowledge-based systems (KBS) that apply rule-based mechanisms to detect code issues, providing precise feedback but struggling with complex, context-dependent cases. More recent work has shifted toward fine-tuning pre-trained language models for code review, enabling broader issue coverage but often at the expense of precision. In this paper, we propose a hybrid approach that combines the strengths of KBS and learning-based systems (LBS) to generate high-quality, comprehensive code reviews. Our method integrates knowledge at three distinct stages of the language model pipeline: during data preparation (DataAugmented Training, DAT), at inference (Retrieval-Augmented Generation, RAG), and after inference (Naive Concatenation of Outputs, NCO). We empirically evaluate our combination strategies against standalone KBS and LBS fine-tuned on a realworld dataset. Our results show that these hybrid strategies enhance the relevance, completeness, and overall quality of review comments, effectively bridging the gap between rule-based tools and deep learning models.

In code review, generating structured and relevant comments is crucial for identifying code issues and facilitating accurate code changes that ensure an efficient code review process. Well-crafted comments not only streamline the code review itself but are also essential for subsequent tasks like code refinement, where the code is modified to satisfy the input review comment. Although various AI-based approaches aimed to automate comment generation, their effectiveness remains limited by the quality of the training data. Existing code review datasets are often noisy and unrefined, posing limitations to the learning potential of AI models and hindering the automation process. To address these challenges, we propose a curation pipeline designed to enhance the quality of the largest publicly available code review dataset. We begin by establishing an evaluation framework, incorporating specific criteria and categories to empirically study the initial quality of the dataset. Using a large language model (LLM)-driven approach, we then apply our curation pipeline to refine the dataset. A comparative analysis of the newly curated dataset, based on the same evaluation framework, demonstrates substantial improvements in the clarity and conciseness of the comments. Additionally, we assess the impact of the curated dataset on automating downstream tasks, specifically comment generation and code refinement. Our findings show that the curated dataset leads to enhanced model performance in generating more accurate comments. Curated comments are also more useful as they lead to more accurate code refinement.

Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribution addresses a notable research gap marked by the absence of a long-term temporal dimension in existing code change datasets, limiting their suitability in lifelong learning scenarios. In contrast, our dataset aims to comprehensively capture code changes across the entire release history of open-source software repositories. In this work, we introduce an initial version of CodeLL, comprising 71 machine-learning-based projects mined from Software Heritage. This dataset enables the extraction and in-depth analysis of code changes spanning 2,483 releases at both the method and API levels. CodeLL enables researchers studying the behaviour of LMs in lifelong fine-tuning settings for learning code changes. Additionally, the dataset can help studying data distribution shifts within software repositories and the evolution of API usages over time.

Web-based applications are software systems that continuously evolve to meet users' needs and to adapt to new technologies. Assuring their quality is then a difficult, but essential task. In fact, a large number of factors can affect their quality. Considering these factors and their interaction involves managing uncertainty and subjectivity inherent to this kind of applications. In this article, we present a probabilistic approach for building Web quality models and the associated assessment method. The proposed approach is based on Bayesian Networks. A model is built following a four-step process consisting in collecting quality characteristics, refining them, building a model structure, and deriving the model parameters. The feasibility of the approach is illustrated on the important quality characteristic of Navigability design . To validate the produced model, we conducted an experimental study with 20 subjects and 40 web pages. The results obtained show that the scores given by the used model are strongly correlated with navigability as perceived and experienced by the users.

Quality assurance of Web applications is usually an informal process. Meanwhile, formal methods have been proven to be reliable means for the specification, verification, and testing of systems. However, the use of these methods requires learning their mathematical foundations, including temporal logics. Specifying properties using temporal logic is often complicated even to experts, while it is a daunting and error prone task for non-expert users. To assist web developers and testers in formally specifying web related properties, we elaborate a library of web specification patterns. The current version of the library of 119 functional and non-functional patterns is a result of scrutinizing various resources in the field of quality assurance of Web Applications, which characterize successful web application using a set of standardized attributes.

With the rapid growth of web technology, more and more software development projects use web collaborations to facilitate the development process. However, web collaboration activity is a complex orchestration. It involves many people work together without the barrier of time and space difference. Therefore, how to efficiently monitor and control web collaboration activity becomes a critical issue in a web-based collaborative software development project. In this paper, we present a novel approach to tackle this difficult problem by means of monitoring collaboration task progress. In addition, we also provide solutions to automate the dynamic control of cooperation, thus to improve the web collaboration performance.