Gender bias is a common and often neglected issue in legal documents. It arises from perceptions or prejudices about the characteristics of a group, or the roles individuals should play in society. This bias can significantly impact the reasoning or outcomes of legal processes, such as judicial rulings. To ensure equal treatment for all individuals, it is crucial to effectively reduce this bias. The first step to reduce bias is to define approaches that can identify manifestations of gender bias. However, these manifestations are not usually easily detectable in text (e.g., through keywords) as they often require detailed contextual analysis, typically done manually by experts. This paper addresses this issue by leveraging natural language processing and machine learning techniques to automate parts of the analysis. Specifically, it proposes a processing pipeline based on text embeddings, binary classification, and the use of large language models (LLMs) to explain classification results. An initial evaluation on a set of judicial rulings shows promising results in terms of precision and recall, along with qualitative insights into the potential of these techniques in the legal domain.

The problem of migrating monolithic applications to microservices has become popular both in industry and academia, particularly when using automated tools to assist developers in the decomposition. While a variety of tools and techniques have been proposed, deciding which is the most appropriate decomposition for a given monolith is challenging because the selected technique can return alternative decompositions depending on how the parameters of that technique are configured. This issue has not received enough attention in the literature, and therefore, developers have to resort to their intuition or use the default parameters reported by the authors of the technique. To investigate this problem further, in this work we perform a study of the parameters and variability of the MicroMiner approach, assessing its parameter sensitivity when dealing with two monolithic applications from the literature. Based on a systematic, data-driven analysis of the landscape of possible decompositions, our results show that, depending on the monolithic application provided as input, certain parameters of MicroMiner have more or less importance on the characteristics of the generated decompositions. These findings provide initial guidelines for developers to configure MicroMiner, as well as other approaches, in order to obtain microservice decompositions with relatively low variability.

Group recommenders aim to provide recommendations that satisfy the collective preferences of multiple users, a challenging task due to the diverse individual tastes and conflicting interests to be balanced. This is often accomplished by using aggregation techniques that select items on which the group can agree. Traditional aggregators struggle with these complexities, as items are chosen independently, leading to sub-optimal recommendations lacking diversity, novelty, or fairness. In this paper, we propose an aggregation technique that leverages Monte Carlo Tree Search (MCTS) to enhance group recommendations. MCTS is used to explore and evaluate candidate recommendation sequences to optimize overall group satisfaction. We also investigate the integration of MCTS with LLMs aiming at better understanding interactions between user preferences and recommendation sequences to inform the search. Experimental evaluations, although preliminary, showed that our proposal outperforms existing aggregation techniques in terms of relevance and beyond-accuracy aspects of recommendations. The LLM integration achieved positive results for recommendations’ relevance. Overall, this work highlights the potential of heuristic search techniques to tackle the complexities of group recommendations.

When developing Java Script (JS) applications, the assessment and selection of JS packages have become challenging for developers due to the growing number of technology options available. Given a technology need, a common developers' strat-egy is to query Web repositories via search engines (e.g., NPM, Google) and shortlist candidate JS packages. However, these engines might return a long list of results. Furthermore, these results should be ranked according to the developer's criteria. To address these problems, we developed a recommender system called AIDT that assists developers in the package selection task. AIDT relies on meta-search and machine learning techniques to infer the relevant packages for a query. An initial evaluation of AIDT showed good search effectiveness. Recently, the emergence of ChatGPT has opened new opportunities for this kind of assistants, as reported by some experiments. Anyway, human developers should judge whether the recommendations (e.g., JS packages) of these tools are fit to purpose. In this paper, we report on a user study in which we used both AIDT and ChatGPT on a sample of JS-related queries, compared their results, and also validated them against developers' criteria and expectations for the task. Our initial findings show that ChatGPT is not yet on par with AIDT or even human efforts for the task at hand, but the model is flexible to be improved and furthermore, it can provide good arguments for its package choices.