A5062526200- Software Engineering Research
- Software Engineering Techniques and Practices
- Big Data and Business Intelligence
- Software Reliability and Analysis Research
- Software System Performance and Reliability
- Advanced Software Engineering Methodologies
- Business Process Modeling and Analysis
- Software Testing and Debugging Techniques
- Semantic Web and Ontologies
- Information and Cyber Security
- Open Source Software Innovations
- Web Application Security Vulnerabilities
- Scientific Computing and Data Management
- Educational Innovations and Challenges
- Artificial Intelligence in Education
- Advanced Malware Detection Techniques
- Service-Oriented Architecture and Web Services
- Innovative Educational Technologies
- Data Quality and Management
- Technology Assessment and Management
- Outsourcing and Supply Chain Management
- Model-Driven Software Engineering Techniques
- Cloud Computing and Resource Management
- Machine Learning and Data Classification
- Safety Systems Engineering in Autonomy
Pontifical Catholic University of Rio de Janeiro
2017-2024
Software (Spain)
2012-2018
Universidade Federal Fluminense
2014-2017
Universidade Federal de Juiz de Fora
2013-2014
Universidade Federal do Rio de Janeiro
2004-2012
Nesta
2012
Diagram (Netherlands)
2012
Methodist University
2007
IMDEA Software
2004
Inesc P&D Brasil
2004
Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which has later complemented with snowball searches. Our conjecture that we can perform even better if combining two approaches, referred to as hybrid strategy. Objective: main objective was compare and evaluate Furthermore, compared some alternative strategies assess whether it possible identify more cost-efficient ways of searching relevant...
As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions.This study investigates effects pandemic developers' wellbeing productivity.A questionnaire survey was created mainly existing, validated scales translated into 12 languages. The data analyzed using non-parametric inferential statistics structural equation modeling.The received 2225 usable responses 53 countries....
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 these SLRs are now potentially not fully up-to-date, and there no standard proposals how update in SE. The objective this paper is propose guidelines best search when updating SE, evaluate using an SLR that was employed during the formulation guidelines. To our guidelines, we compare discuss outcomes from applying...
Requirements Engineering (RE) has established itself as a software engineering discipline during the past decades. While researchers have been investigating RE with plethora of empirical studies, attempts to systematically derive an empirically-based theory in context just recently started. However, such is needed if we are define and motivate guidance performing high quality research practice. We aim at providing valid foundation for RE, which helps engineers establish effective efficient...
Background: A Systematic Literature Review (SLR) is a methodology used to aggregate relevant evidence related one or more research questions. Whenever new published after the completion of SLR, this SLR should be updated in order preserve its value. However, updating SLRs involves significant effort. Objective: The goal paper investigate application forward snowballing support update SLRs. Method: We compare outcomes an achieved using versus search-based approach, i.e., searching for studies...
Together with many success stories, promises such as the increase in production speed and improvement stakeholders' collaboration have contributed to making agile a transformation software industry which companies want take part. However, driven either by natural expected evolution or contextual factors that challenge adoption of methods prescribed their creator(s), processes practice mutate into hybrids over time. Are these still agile? In this article, we investigate question: what makes...
Machine learning (ML) has become a core feature for today's real-world applications, making it trending topic the software engineering community. Requirements Engineering (RE) is no stranger to this and its main conferences have included workshops aiming at discussing RE in context of ML. However, current research on intersection between ML mainly focuses using techniques support activities rather than exploring how can improve development ML-based systems. This paper concerns systematic...
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold Machine Learning (ML) projects, which involve iterative experimentation with data, models, algorithms. However, ML components are often developed by data scientists diverse educational backgrounds, potentially resulting code that doesn't adhere to best practices. [Goal] In order better understand this...
[Background] Systematic Literature Reviews (SLRs) are one of the important pillars when employing an evidence-based paradigm in Software Engineering. To date most SLRs have been conducted using a search strategy involving several digital libraries. However, significant issues reported for libraries and applying such requires substantial effort. On other hand, snowballing has recently arisen as potentially more efficient alternative or complementary solution. Nevertheless, it relevant seed...
Background: Code refactoring aims to improve code structures via transformations. A single transformation rarely suffices fully remove smells that reveal poor structures. Most transformations are applied in batches, i.e. sets of interrelated transformations, rather than isolation. Nevertheless, empirical knowledge on batch application, or refactoring, is scarce. Such scarceness helps little current practices. Aims: We analyzed 57 open and closed software projects. aimed understand...
Microservices have become a popular architectural style for data-driven applications, given their ability to functionally decompose an application into small and autonomous services achieve scalability, strong isolation, specialization of database systems the workloads data formats each service. Despite accelerating industrial adoption this style, investigation state practice challenges practitioners face regarding management in microservices is lacking. To bridge gap, we conducted...
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations experiencing friction the adoption of ML-based technologies, mainly due to shortage ML professionals. In this context, Automated (AutoML) techniques have been presented as a promising solution democratize adoption. Objective. We aim provide an overview evidence on benefits...
[Background] The rapidly changing business environments in which many companies operate is challenging traditional Requirements Engineering (RE) approaches. This gave rise to agile approaches for RE. Security, at the same time, an essential non-functional requirement that still tends be difficult address development contexts. Given fuzzy notion of "agile" context RE and difficulties appropriately handling security requirements, overall understanding how handle requirements vague. [Objective]...
The prevalence of design problems may cause re-engineering or even discontinuation the system. Due to missing, informal outdated documentation, developers often have rely on source code identify problems. Therefore, analyze different symptoms that manifest in several elements, which quickly turn into a complex task. Although researchers been investigating techniques help identifying problems, there is little knowledge how actually proceed In order tackle this problem, we conducted...
Context: Data-intensive systems, a.k.a. big data systems (BDS), are software that handle a large volume of in the presence performance quality attributes, such as scalability and availability. Before advent management (e.g. Cassandra) frameworks Spark), organizations had to cope with volumes custom-tailored solutions. In particular, decade ago, Tecgraf/PUC-Rio developed system monitor truck fleet real-time proactively detect events from positioning received. Over years, evolved into complex...