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Researcher profile

Marcos Kalinowski

Marcos Kalinowski contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Software EngineeringMachine LearningArtificial IntelligenceDigital LibrariesInformation RetrievalMultiagent Systems

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Published work

10 published item(s)

preprint2026arXiv

A Research Agenda on Agents and Software Engineering: Outcomes from the Rio A2SE Seminar

The rise of agentic AI is reshaping software engineering in two intertwined directions: agents are increasingly applied to support software engineering tasks, and Agentic AI systems themselves are complex systems that require re-thinking currently established software engineering practices. To chart a coherent research agenda covering the two directions, we organized the A2SE seminar in Rio de Janeiro, bringing together 18 experts from academia and industry. Through structured presentations, collaborative topic clustering, and focused group discussions, participants identified six thematic areas: Governance, Software Engineering for Agents, Agents for Software Architecture, Quality and Evaluation, Sustainability, and Code, and they prioritized short-term and long-term research directions for each. This paper presents the resulting community-driven, opinionated research agenda, offering the SE community a structured foundation for coordinating efforts at this critical juncture.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Catalogue of Concerns for Specifying Machine Learning-Enabled Systems

Requirements engineering (RE) activities for machine learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus on RE for ML can help to develop more reliable ML-enabled systems. Based on insights collected from previous work and industrial experiences, we propose a catalogue of 45 concerns to be considered when specifying ML-enabled systems, covering five different perspectives we identified as relevant for such systems: objectives, user experience, infrastructure, model, and data. Examples of such concerns include the execution engine and telemetry for the infrastructure perspective, and explainability and reproducibility for the model perspective. We conducted a focus group session with eight software professionals with experience developing ML-enabled systems to validate the importance, quality and feasibility of using our catalogue. The feedback allowed us to improve the catalogue and confirmed its practical relevance. The main research contribution of this work consists in providing a validated set of concerns grouped into perspectives that can be used by requirements engineers to support the specification of ML-enabled systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Predicting IMDb Rating of TV Series with Deep Learning: The Case of Arrow

Context: The number of TV series offered nowadays is very high. Due to its large amount, many series are canceled due to a lack of originality that generates a low audience. Problem: Having a decision support system that can show why some shows are a huge success or not would facilitate the choices of renewing or starting a show. Solution: We studied the case of the series Arrow broadcasted by CW Network and used descriptive and predictive modeling techniques to predict the IMDb rating. We assumed that the theme of the episode would affect its evaluation by users, so the dataset is composed only by the director of the episode, the number of reviews that episode got, the percentual of each theme extracted by the Latent Dirichlet Allocation (LDA) model of an episode, the number of viewers from Wikipedia and the rating from IMDb. The LDA model is a generative probabilistic model of a collection of documents made up of words. Method: In this prescriptive research, the case study method was used, and its results were analyzed using a quantitative approach. Summary of Results: With the features of each episode, the model that performed the best to predict the rating was Catboost due to a similar mean squared error of the KNN model but a better standard deviation during the test phase. It was possible to predict IMDb ratings with an acceptable root mean squared error of 0.55.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Continuous Systematic Literature Review in Software Engineering

Context: New scientific evidence continuously arises with advances in Software Engineering (SE) research. Conventionally, Systematic Literature Reviews (SLRs) are not updated or updated intermittently, leaving gaps between updates, during which time the SLR may be missing crucial new evidence. Goal: We propose and evaluate a concept and process called Continuous Systematic Literature Review (CSLR) in SE. Method: To elaborate on the CSLR concept and process, we performed a synthesis of evidence by conducting a meta-ethnography, addressing knowledge from varied research areas. Furthermore, we conducted a case study to evaluate the CSLR process. Results: We describe the resulting CSLR process in BPMN format. The case study results provide indications on the importance and feasibility of applying CSLR in practice to continuously update SLR evidence in SE. Conclusion: The CSLR concept and process provide a feasible and systematic way to continuously incorporate new evidence into SLRs, supporting trustworthy and up-to-date evidence for SLRs in SE.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Perspective-Based Specification of Machine Learning-Enabled Systems

Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks, experimenting with algorithms, evaluating models, capturing data from users, among others. Literature has shown that ML-enabled systems are rarely built based on precise specifications for such concerns, leading ML teams to become misaligned due to incorrect assumptions, which may affect the quality of such systems and overall project success. In order to help addressing this issue, this paper describes our work towards a perspective-based approach for specifying ML-enabled systems. The approach involves analyzing a set of 45 ML concerns grouped into five perspectives: objectives, user experience, infrastructure, model, and data. The main contribution of this paper is to provide two new artifacts that can be used to help specifying ML-enabled systems: (i) the perspective-based ML task and concern diagram and (ii) the perspective-based ML specification template.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Efficient Approach for Reviewing Security-Related Aspects in Agile Requirements Specifications of Web Applications

Defects in requirements specifications can have severe consequences during the software development lifecycle. Some of them may result in poor product quality and/or time and budget overruns due to incorrect or missing quality characteristics, such as security. This characteristic requires special attention in web applications because they have become a target for manipulating sensible data. Several concerns make security difficult to deal with. For instance, security requirements are often misunderstood and improperly specified due to lack of security expertise and emphasis on security during early stages of software development. This often leads to unspecified or ill-defined security-related aspects. These concerns become even more challenging in agile contexts, where lightweight documentation is typically produced. To tackle this problem, we designed an approach for reviewing security-related aspects in agile requirements specifications of web applications. Our proposal considers user stories and security specifications as inputs and relates those user stories to security properties via Natural Language Processing. Based on the related security properties, our approach identifies high-level security requirements from the Open Web Application Security Project (OWASP) to be verified, and generates a reading technique to support reviewers in detecting defects. We evaluate our approach via three experiment trials conducted with 56 novice software engineers, measuring effectiveness, efficiency, usefulness, and ease of use. We compare our approach against using: (1) the OWASP high-level security requirements, and (2) a perspective-based approach as proposed in contemporary state of the art. The results strengthen our confidence that using our approach has a positive impact (with large effect size) on the performance of inspectors in terms of effectiveness and efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Challenges in Survey Research

While being an important and often used research method, survey research has been less often discussed on a methodological level in empirical software engineering than other types of research. This chapter compiles a set of important and challenging issues in survey research based on experiences with several large-scale international surveys. The chapter covers theory building, sampling, invitation and follow-up, statistical as well as qualitative analysis of survey data and the usage of psychometrics in software engineering surveys.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineering

Context: Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering

Context: When conducting a Systematic Literature Review (SLR), researchers usually face the challenge of designing a search strategy that appropriately balances result quality and review effort. Using digital library (or database) searches or snowballing alone may not be enough to achieve high-quality results. On the other hand, using both digital library searches and snowballing together may increase the overall review effort. Objective: The goal of this research is to propose and evaluate hybrid search strategies that selectively combine database searches with snowballing. Method: We propose four hybrid search strategies combining database searches in digital libraries with iterative, parallel, or sequential backward and forward snowballing. We simulated the strategies over three existing SLRs in SE that adopted both database searches and snowballing. We compared the outcome of digital library searches, snowballing, and hybrid strategies using precision, recall, and F-measure to investigate the performance of each strategy. Results: Our results show that, for the analyzed SLRs, combining database searches from the Scopus digital library with parallel or sequential snowballing achieved the most appropriate balance of precision and recall. Conclusion: We put forward that, depending on the goals of the SLR and the available resources, using a hybrid search strategy involving a representative digital library and parallel or sequential snowballing tends to represent an appropriate alternative to be used when searching for evidence in SLRs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

When to Update Systematic Literature Reviews in Software Engineering

[Context] Systematic Literature Reviews (SLRs) have been adopted by the Software Engineering (SE) community for approximately 15 years to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially outdated, and there are no systematic proposals on when to update SLRs in SE. [Objective] The goal of this paper is to provide recommendations on when to update SLRs in SE. [Method] We evaluated, using a three-step approach, a third-party decision framework (3PDF) employed in other fields, to decide whether SLRs need updating. First, we conducted a literature review of SLR updates in SE and contacted the authors to obtain their feedback relating to the usefulness of the 3PDF within the context of SLR updates in SE. Second, we used these authors feedback to see whether the framework needed any adaptation; none was suggested. Third, we applied the 3PDF to the SLR updates identified in our literature review. [Results] The 3PDF showed that 14 of the 20 SLRs did not need updating. This supports the use of a decision support mechanism (such as the 3PDF) to help the SE community decide when to update SLRs. [Conclusions] We put forward that the 3PDF should be adopted by the SE community to keep relevant evidence up to date and to avoid wasting effort with unnecessary updates.

0 citations0 reviewsNo code linkedOpen access