Abstract Scrum is widely adopted in software project management due to its adaptability and collaborative nature. The recent emergence of Large Language Models (LLMs) has created new opportunities to support knowledge-intensive Scrum practices. However, existing research has largely focused on technical activities such as coding and testing, with limited evidence on the use of LLMs in management-related Scrum activities. In this study, we investigate the use of LLMs in Scrum management activities through a survey of 70 Brazilian professionals. Among them, 49 actively use Scrum, and 33 reported using LLM-based assistants in their Scrum practices. The results indicate a high level of proficiency and frequent use of LLMs, with 85% of respondents reporting intermediate or advanced proficiency and 52% using them daily. LLM use concentrates on exploring Scrum practices, with artifacts and events receiving targeted yet uneven support, whereas broader management tasks appear to be adopted more cautiously. The main benefits include increased productivity (78%) and reduced manual effort (75%). However, several critical risks remain, as respondents report ‘almost correct’ outputs (81%), confidentiality concerns (63%), and hallucinations during use (59%). This work provides one of the first empirical characterizations of LLM use in Scrum management, identifying current practices, quantifying benefits and risks, and outlining directions for responsible adoption and integration in Agile environments.

Abstract Background: Ensuring the quality of user stories is vital to Agile Software Development. Rule-based tools like AQUSA, based on the Quality User Story (QUS) framework, offer reliable structural checks but struggle with context-sensitive or pragmatic issues. Large Language Models (LLMs) have emerged as potential alternatives, yet prior studies often rely on small datasets, older models, or lack direct comparison with rule-based baselines. Objective: This study aims to assess the effectiveness of modern LLMs relative to a rule-based tool (AQUSA) for detecting defects in user stories, considering both structural and contextual dimensions. Method: We conduct a large-scale comparative evaluation involving AQUSA and three GPT-family LLMs (GPT-5, GPT-5-mini, and GPT-4), using 182 user stories drawn from three industrial datasets. We apply both quantitative metrics (precision, recall, F1-score) and qualitative analysis of feedback clarity and defect relevance. Results: GPT-5-mini achieved the highest recall (0.81) and overall F1-score (0.62), while AQUSA attained the highest precision (0.61) with significantly fewer false positives. GPT-5 showed high hallucination rates and instability; GPT-4 was overly conservative, leading to under-detection of defects. Conclusion: Neither rule-based nor GPT-family LLM-based approaches suffice in isolation. Rule-based tools enforce structural rigor, while LLMs capture nuanced linguistic and pragmatic flaws. We advocate a hybrid “Dual-gate” strategy—using AQUSA for structural validation followed by lightweight LLMs for contextual refinement—to improve the reliability and scalability of user story quality assessment in agile environments.

Distributed Denial of Service (DDoS) attacks are a global security concern, driven by the rapid digital transformation and the widespread adoption of 5G networks across industries. As networks connect more devices to the Internet, enabling use cases such as the industrial Internet of Things (IoT), the attack surface grows. Thus, applying deep learning techniques presents a promising approach for detecting DDoS attacks. For example, using the CIC-DDoS2019 dataset, a Convolutional Neural Network (CNN) model with multi-head attention achieved 97% accuracy. With a 5G slice-based dataset, the model achieved an accuracy of 91%. These results highlight the potential of CNNs as a scalable approach to mitigating DDoS threats. Beyond improving detection accuracy, this method enables real-time identification of attacks, supporting a proactive defense against evolving cybersecurity threats. The findings demonstrate that CNNs can help secure critical infrastructure, including IoT devices, telecommunications systems, and other networked services. Implementing such solutions enhances network security and strengthens resilience against emerging challenges.

Summary Model‐based testing (MBT) relies on models of the system's behaviour to generate abstract tests. Testers can reuse formal models using MBT to increase confidence in critical systems (e.g., medical and avionic systems). In this article, we investigate the current abstract test generation approaches for CPN to provide insights for testers who need to select a suitable one when applying the MBT using CPN. We conduct a systematic literature review to investigate the existing abstract test generation approaches designed for CPN. Subsequently, focusing on specific implementations and advantages/disadvantages, we experiment with formal models of medical systems during our empirical analysis to improve the discussion on existing abstract test generation approaches for CPN. Our study shows that CPN provides reliable tests quickly, depending on the abstract test generation approach applied.

Technical Debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefits but might hurt the long-term health of a software system. With the increasing amount of data generated when performing software development activities, an emergent research field has gained attention: applying Intelligent Techniques to solve Software Engineering problems. Intelligent Techniques were used to explore data for knowledge discovery, reasoning, learning, planning, perception, or supporting decision-making. Although these techniques can be promising, there is no structured understanding related to their application to support Technical Debt Management (TDM) activities. Within this context, this study aims to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. To this end, we performed a Systematic Mapping Study (SMS) to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. In total, 150 primary studies were identified and analyzed, dated from 2012 to 2021. The results indicated a growing interest in applying Intelligent Techniques to support TDM activities, the most used: Machine Learning and Reasoning under uncertainty. Intelligent Techniques aimed to assist mainly TDM activities related to identification, measurement, and monitoring. Design TD, Code TD, and Architectural TD are the TD types in the spotlight. Most studies were categorized at automation levels 1 and 2, meaning that existing approaches still require substantial human intervention. Symbolists and Analogizers are levels of explanation presented by most Intelligent Techniques, implying that these solutions conclude a general truth after considering a sufficient number of particular cases. Moreover, we also cataloged the empirical research types, contributions, and validation strategies described in primary studies. Based on our findings, we argue that there is still room to improve the use of Intelligent Techniques to support TDM activities. The open issues that emerged from this study can represent future opportunities for practitioners and researchers.

Technical Debt (TD) refers to the consequences of taking shortcuts when developing software. Technical Debt Management (TDM) becomes complex since it relies on a decision process based on multiple and heterogeneous data, which are not straightforward to be synthesized. In this context, there is a promising opportunity to use Intelligent Techniques to support TDM activities since these techniques explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can be used for improving TDM activities, there is no empirical study exploring this research area. This study aims to identify and analyze solutions based on Intelligent Techniques employed to support TDM activities. A Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities, being Machine Learning, Reasoning Under Uncertainty, and Natural Language Processing the most recurrent ones. Identification, measurement, and monitoring were the more recurrent TDM activities, whereas Design, Code, and Architectural were the most frequently investigated TD types. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research.

Agile Software Development (ASD) has become the mainstream software development method of choice. Its core fundamentals are based on Teamwork factors and the higher value of individuals and their interactions over processes and tools. However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct. Driven by this problem, we present a thematic network that synthesizes the information presented in the literature, and eases knowledge sharing by defining a terminology. The thematic network is the result of the following process: (i) studies definition to be used as data source through a literature review; (ii) data extraction from these studies; (iii) data translation into codes; (iv) codes translation into themes; (v) creation of higher-order themes model; and (vi) assessment of synthesis trustworthiness. The resulting thematic network comprises four higher-order themes: Cohesion, Orientation, Shared Leadership, and Autonomy. We also evaluate the applicability of the identified themes in ASD Teamwork constructs in the literature. We concluded that the constructed thematic network can be generalized to ASD, and used as basis by researchers who intend to explore ASD Teamwork. Further, practitioners can use our results to understand agile teams’ dynamics better and improve their efficiency.

Agile Software Development (ASD) has become the mainstream software development method of choice.Its core fundamentals are based on Teamwork factors and the higher value it gives to individuals and their interactions over processes and tools.Teamwork and human factors have been addressed as essential topics in the literature, and researchers have stated the importance of measuring it to increase the chances of success of ASD projects.However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct.Driven by this problem, this paper presents a thematic network that defines the themes (i.e., factors) that should be considered when addressing ASD Teamwork.The ASD Teamwork thematic network is the result of a process that consisted of (i) defining the studies used as a data source through a literature review; (ii) extracting data from these studies; (iii) translating this data into codes; (iv) translating the codes into themes; (v) creating the model of higher-order themes; and, (vi) assessing the trustworthiness of the synthesis.The resulting thematic network comprises four higher-level themes: Cohesion, Orientation, Shared Leadership, and Autonomy.We believe that the constructed thematic network can be generalized to ASD and used as the basis by researchers who intend to explore ASD Teamwork.Further, practitioners can use our results to understand agile teams' dynamics better and improve their efficiency.

Over the years, researchers have explored the influence of human factors in software engineering, showing that the team members' personalities might affect teamwork.However, it is challenging to measure software engineers' personalities due to the number of available psychometric instruments and the possibility of using different scales and classifications.Our study compares the personality traits measured by three psychometric instruments used in Software Engineering: Big Five Inventory (BFI), 16 Personality Factors (16PF), and Context Cards (CC).For this purpose, we executed an empirical study in which we collected data from 29 software developers for each of the evaluated instruments.As a result, we identified a moderate correlation between BFI and 16PF, confirming the current stateof-the-art.For the remaining combinations, there was a weak correlation.As implications for this research, there is a need to empirically evaluate BFI and CC (context-specific survey) in terms of construct validity since they have moderate to low correlation.

Using knowledge-based systems for helping agile teams to improve their performance is not a fact in the industry.In previous work, we have presented Kaizen, a knowledge-based Bayesian network for assisting Scrum teams in diagnosing their value stream in light of the predicted Customer Satisfaction and, consequently, improve their performance.This study assesses Kaizen's accuracy to predict Customer Satisfaction using realworld data.We adopted Kaizen for one software development company and collected data from 18 projects using an online questionnaire.We collected two types of data: inputs for Kaizen and the expected Customer satisfaction.We used the first type of collected data as inputs for Kaizen to calculate the predicted Customer satisfaction.Then, we assessed Kaizen's accuracy by comparing the predicted (i.e., calculated) and expected (i.e., collected) Customer satisfaction using face value and the average Brier score.Considering the face value, Kaizen predicted Customer Satisfaction correctly for 14 out of the 18 projects.The average Brier Score was 0.16.The model predicts, with satisfactory accuracy, the Customer Satisfaction and systemizes the process for Scrum teams to self-diagnose, enabling for causal analysis and supporting their continuous improvement.

The Internet-of-Things (IoT) scenarios commonly present security and privacy concerns, either due to the processing constraints of devices or the employment of external servers to process and store data, for instance, in cloud-based IoT applications. In this sense, to protect data and decrease user distrust in external entities, security technologies are of utmost importance. Trusted execution environments (TEEs), which process data in an isolated and protected region of memory, are among these technologies. We focus on a trusted architecture solution based on the use of TEEs and the application of authentication, authorization, and encryption mechanisms to protect data in IoT applications. We specified the trusted IoT architecture (TIoTA) using hierarchical colored Petri nets, and performed simulations and model checking of key security properties related to desired and prohibited behaviors, enabling model-based testing. This article enhances the state of the art by providing project artifacts (e.g., executable and parametric models) for correctly implementing the TIoTA and by presenting evidence that the usage of the architecture can improve security and privacy.

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services.

Insulin infusion pumps are safety-critical systems that require the evaluation of government regulatory agencies (e.g., FDA) before commercialization. This paper presents a Coloured Petri Nets (CPN) reference model of insulin infusion pump control systems to assist manufacturers to generate safety evidence and evaluate quality for certification purposes. It also describes a case study on the ACCD-CHECK Spirit system version 2.XX to evaluate the CPN model. Therefore, the reference model is available and verified. Additionally, the case study is useful to show how manufacturers can reuse it during a certification process. The main contribution of this paper consists on a project artifact to decrease costs and development time of insulin infusion pump control systems, in addition to increase confidence on safety properties.

Bio-medical signal acquisition systems (or simply bio-medical systems) are commonly used during the diagnosis, monitoring, and treatment of patients. The Electrocardiograph (ECG) is an example of this type of system. It is necessary to submit bio-medical systems to regulatory agencies for certification. Regulatory agencies conduct evaluations seeking the commercialization of high-quality systems. In a previous work, we specified an ECG model by integrating the Coloured Petri Nets (CPN) and MATLAB® tools. However, this introduced an external component dependency that limited the application and re-usability of the model during the certification process. We present in this paper a mixed-radix Fast Fourier Transform (FFT) algorithm based on CPN aiming to assist the design of bio-medical systems using CPN. We improved the design of an ECG model to show how the mixed-radix FFT algorithm solves the previous limitations, and to evaluate the effectiveness of a CPN/ML implementation. The comparison of a system under development with similar certified systems is commonly accepted by regulatory agencies. The replacement of a similar system by the open source CPN/Tools software and the available reference model can decrease costs, and development and certification time.

Accurate effort estimation is an important part of the software process.In Agile Software Development, the techniques for predicting effort are mostly based on expert judgment, but there are approaches based on Machine Learning.The theme continues to be challenging and a subject of further studies given the difficulty of finding accurate solutions to the problem.This paper proposes and evaluates a tool based on the decision tree method for effort estimation in agile projects.We evaluated our tool given its accuracy and ease of use collecting data from four projects.To evaluate the accuracy, we compared the values of Magnitude of Relative Error from the teams' estimations with the values provided by the tool.To evaluate the ease of use, we used the Technology Acceptance Mode.The initial results show that the tool can be reliably used with minimal training.In terms of accuracy, the tool achieved lower error compared to the estimates provided by the teams (mean: 19.05% vs 33.32%), and the evaluation means in TAM were higher than 4.0 in ten of the eleven variables analyzed on a Likert scale.From this work, we conclude that estimation by decision tree is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

The development of tools and smart devices has grown exponentially over the past few years, most due to the appearance of the Internet of Things (IoT).The large number of different connected devices highlighted challenges such as interoperability, scalability and reliability.In this scenario, for the development of new services and applications, it is necessary to use software platforms that ease the deployment and validation based on those challenges.With this, arises the need for middlewares, platforms that provide an environment for the development of such applications.In this way, in order to provide a simple, lightweight and dynamic approach, this article presents a study and development of an architecture that allows the communication, control and monitoring of IoT devices using an intuitive manner through an actor model.This is possible through the integration of the Calvin framework, the MQTT protocol and the OCF data model, providing an interoperable, reliable, dynamic and remote communication.A smart home environment was used for validation showing the relevance of the proposal.

One challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPTs), which can be learned from data or elicited from domain experts.In practice, for large-scale BN it is common not to have enough data for learning and elicitation from experts is unfeasible.Previous work proposed a solution to this problem: the Ranked Nodes Method (RNM).However, this solution needs to be applied by a RNM expert who, through the elicitation of expert judgement, identifies the necessary parameters for the RNM algorithm to generate the NPTs.Hence, this paper presents a novel approach to define NPT using the RNM with no ranked nodes-specific knowledge.The solution is named Simulated Bayesian Network Expert (SBNE).It consists of eliciting a subset of the NPT from the domain experts which is used as input to an algorithm that estimates the optimal parameters for the RNM to generate the NPTs.To validate our solution, we conducted an experiment with multiple domain experts and compared the results with other methods.Our solution outperformed the other methods (producing NPTs at least 12% more accurate) and is, therefore, a promising approach to apply RNM without relying on RNM experts.

Due to the need for flexibility to requirements changes, agile software development methods have been attracting the attention of academic and industrial domains.Unlike traditional approaches, agile methods focus on the rapid delivery of business value to customers through empirical and incremental development processes.Despite being effective in delivering quality functional requirements, agile practices generally neglect non-functional requirements until the later stages of software development.However, neglecting non-functional requirements during requirements analysis can lead to project failures.In this paper, we present the NFRec tool, which aims to support software developers in the elicitation of non-functional requirements in the context of agile software development.Additionally, we report the results from a case study to evaluate the acceptance of the NFRec tool from the point of view of software developers of four projects from a Brazilian software company.To gather information about the tool acceptance, we applied a questionnaire based on the indicators from the Technology Acceptance Model.Overall, the four teams considered the NFRec tool useful and easy to use for supporting the management of non-functional requirements in agile projects.

Software metrics are usually used for quantification, not giving the necessary support for decision making.To increase their usefulness, it is necessary to give them meaning through the definition of significant thresholds.Despite its importance, the state of the art on threshold derivation is mostly based on data-driven approaches.This paper presents a systematic approach to define thresholds for metrics in the absence of data and based on eliciting knowledge from experts.The proposed approach is based on identifying context factors that influence the thresholds for a given metric and is supported by fuzzy logic concepts to model the crisp value (i.e., collected data) into a linguistic variable (i.e., interpreted information).We present context factors elicited from three experts for the metrics code coverage, static code analysis warnings count and defect count.Further, we present cases on how to implement the proposed approach.As a result, we conclude that the approach is promising.

The swarm concept in Internet of Things (IoT) describes the cooperation of independent and heterogeneous devices to execute tasks. Components of a swarm system must seamlessly discover other objects. Semantic discovery is an approach to perform this task and can be performed either by automated or manual ways, but not often taking account information trust. This article presents a middleware that performs automatic IoT semantic discovery and determining information trust. Experimental results, carried out with real data from smart cities projects are presented.

Search-Based software engineering (SBSE) deals with metaheuristic search-based optimization techniques to provide solutions for complex problems.A popular problem in literature is the team formation problem (TFP), which consists of finding the best allocation of human resources to a software development project.This problem is recognized as NP-hard and it is more complex in companies that carry out multiple projects.This paper presents an effective and automated approach to allocate multiple developers into multiple teams to maximize the technical compatibility between them.The approach consists of an SBSE method that uses Genetic Algorithm to simultaneously build multiple teams, using data from tag-based profiles.We conducted an empirical evaluation using data from eight realworld software projects of a Brazilian company.The results indicate that tag-based profiles is a promising information source to represent technical knowledge, since the suggested teams were considered to have the proper skills to the attend the technical demand of the projects.The approach was able to reach high levels of satisfaction, delivering teams in an effective and automated way.Although, further investigation needs to be conducted to reach stronger conclusions.

One of the main issues of an agile software project is how to accurately estimate development effort.In 2014, it was published a Systematic Literature Review (SLR) regarding this subject.The authors of this SLR analyzed works from 2001 to 2013 and reached the number of 25 relevant papers.Therefore, the goal of our work is to provide an updated review of the state of the art based on this reference SLR work.We applied a Forward Snowballing approach, in which our seed set are the former SLR and its selected papers.We identified changes in this new review comparing it with the reference SLR: XP methodology was mentioned in just a few works; Use Case Points (UCP) method and Case Points as size metric were not found.We also observed a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD).Finally, we identified that in the reference SLR there is a gap in terms of agreement on suitable cost drivers.Thus, in our updated review, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation.

Background: The focus of Agile software development (ASD) is different than plan-driven development, requiring new software process improvement (SPI) paradigms.Objective: To identify and synthesize the possible gaps of Agile improvement solutions (AIS) given their focus on people factors, report of successful adoption in industry projects and availability of tool support.Method: We applied a Systematic Literature Review of studies published up to (and including) 2017 through backward and forward snowballing given a start set.Results: In total, we evaluated 55 papers, of which 44 included AIS and the main findings are: 1) 26 consider teamwork factors; 2) 21 were applied on industry; 3) 10 out of these 21 presented evidence of increase in company performance; and 4) 19 of the solutions are for the purpose of adoption, 18 for assessment and 8 are maturity models.Conclusion: The main implication for this research is a need for more and better empirical studies documenting and evaluating AIS.For the industry, the review provides a map of current AIS approaches and can be used as a starting point to adopt agile SPI.

Agile software development focuses on quick delivery and flexibility to change.Despite being effective in delivering quality functional requirements, agile practices tend to neglect non-functional requirements until the later stages of software development.This work focuses on Scrum, the most popular agile method, and presents a non-functional requirements recommendation system to support Scrum practitioners on their early identification.The solution is based on instrumenting the Scrum process to extract useful data and the use of collaborative filtering and item recommendation.To evaluate the recommendations, we conducted off-line experiments with data collected from 12 Scrum practitioners through a survey.The data was analyzed using 10fold cross-validation.As a result, our proposed solution showed a recall rate of up to 81%, which indicates that it is a promising approach to recommend non-functional requirements given a set of functional requirements identified by project stakeholders.

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected.

Internet of Things (IoT) resources cooperate with themselves for requesting and providing services. In heterogeneous and complex environments, those resources must trust each other. On-Off attacks threaten the IoT trust security through nodes performing good and bad behaviors randomly, to avoid being rated as a menace. Some countermeasures demand prior levels of trust knowledge and time to classify a node behavior. In some cases, a malfunctioning node can be mismatched as an attacker. In this paper, we introduce a smart trust management method, based on machine learning and an elastic slide window technique that automatically assesses the IoT resource trust, evaluating service provider attributes. In simulated and real-world data, this method was able to identify On-Off attackers and fault nodes with a precision up to 96% and low time consumption.

Background: Definition of Done (DoD) is a Scrum practice that consists of a simple list of criteria that adds verifiable or demonstrable value to the product. It is one of the most popular agile practices and assures a balance between short-term delivery of features and long-term product quality, but little is known of its actual use in Agile teams.

Scaffolding is an approach used by some modern web frameworks in order to generate an initial version of applications code based on domain model meta data.Since this temporary code should be customized by programmers to implement real systems, its quality metrics are important aspects.In this paper, a methodology is proposed and applied in order to relate domain model size and a quality metric -amount of duplicated code -focusing on Graphical user interface implementation.Results show that code duplication grows at least linearly with the growth of the number of entities in domain model.There are also some scenarios where quadratic proportions were found.These observations suggest that, for large domain models, code quality and its evolution would be affected when scaffolding frameworks are used.

Agile software development has been increasingly used to satisfy the need to respond to fast moving market demand and gain market share.Scrum, which is a project management framework, dominates as the most popular agile method.In the literature, there are a number of solutions to customize and assess Scrum-based agile methods, but they are limited to focus only on process factors, assume a predefined set of practices or rely only on subjective evaluation.This paper presents a framework to build a Bayesian Network to assist on the assessment of Scrum-based software development methods.The BN models the main entities of the software development process and can be complemented with software practices and metrics.To evaluate the completeness of our solution, we performed simulations to check if the proposed framework diagnoses 14 known Scrum anti-patterns extracted from the literature.12 antipatterns were directly detected, 1 was indirectly detected by the BN and 1 was considered as invalid.We concluded that the proposed solution is complete to detect the major flaws of Scrum-based software development methods and can be used to assist on the configuration, adoption and continuous improvement of Scrum teams.

Software metrics have a fundamental role in the process of software quality management.However, in most cases, they are only used to quantify attributes, not supporting decision-making during the software life cycle.To support decision-making, it is necessary to give them by defining thresholds.In the literature, several approaches have been proposed with this purpose.On the other hand, most of them do not consider context factors such as the domain.Given this, in this paper, we evaluate if context factors influence the definition of thresholds for software metrics.Our work is restricted to Chidamber and Kemerer metrics, due to availability of data.We conducted an empirical study composed of two quasi-experiments.Each quasi-experiment uses an approach presented in the literature to define thresholds for software metrics, with the defined thresholds as the dependent variable.As the factor, we used a variable with two possible treatments: to consider the context or not.To define context, we used factors presented in the literature.As the objects of study, we used the source code of fifteen Java-based open-source projects.For measurement purposes, we used the six original Chidamber and Kemerer metrics.For both quasi-experiments, the accuracy of the definition of thresholds improved by considering the context.Therefore, we concluded that context factors influence the definition of the threshold for Chidamber and Kemerer metrics, which is an indicator that it influences other software metrics.

Software metrics are essential resources in software enterprises.They can be used to support decision-making and, consequently, reduce costs, improve the productivity of the team and the quality of products delivered.On the other hand, this is only possible if the metrics are valid.Although there are studies related to software metrics validity, none present a solution to represent the uncertainties of the metrics selected to measure the attributes of the entities.In this paper, we present a process to build Bayesian networks to represent the uncertainties of software metrics-based models.The proposed solution is composed of two activities and focuses on the selection and validation of metrics to construct the Bayesian networks.We validated the model with simulated scenarios.Given the successful results, we concluded that the proposed solution is promising.This paper complements the state of the art by showing how to complement a popular metric selection technique, GQM, with information to model uncertainties of the metrics using the concepts of metric validation and Bayesian networks.

Abstract Scrumis a simple process to understand, but hard to adopt. Therefore, there is a need for resources to assist on its adoption. In this paper, we present the process followed to build aBayesian networkto assist on the assessment of the quality of the software process in the context ofScrumprojects. The model provides data to helpScrum Masterslead the improvement of business value delivery ofScrumteams. The process is divided into 2 phases. In the first phase, we built theBayesian networkbased on expert knowledge extracted from the literature and experts. We used a top‐down approach and reasoning to define the key metrics necessary to build the models and their relationships. In the second phase, we updated theBayesian networkbased on limitations of the first version. We validated theBayesian networkinferences with 10 simulated scenarios. Comparing both versions, for all scenarios, we improved the accuracy of the inferences. Therefore, we concluded that theBayesian networksadequately representScrumprojects from the viewpoint of theScrumMaster. Finally, the model built is in conformance with agile methods tailoring and can be adapted to anyScrumteam.

Activity recognition has been widely studied in ubiquitous computing since it can be used in several application domains, such as fall detection and gesture recognition.Initially, works in this area were based on research-only devices (bodyworn sensors).However, with advances in mobile computing, current research focuses on mobile devices, mainly, smartphones.These devices provide Internet access, processing, and various sensors, such as accelerometer and gyroscope, which are useful resources for activity recognition.Therefore, many studies use smartphones as data source.Additionally, some works have already considered the use of wristbands and specially-designed watches, but fewer investigate the latest marketable wearable devices, such as smartwatches, which are less intrusive and can provide new opportunities to complement smartphone data.Moreover, for the best of our knowledge, no previous work experimentally evaluates the impact caused by the combination of sensor data from smartwatches and smartphones on the accuracy of activity recognition approaches.Therefore, the main goal of this experimental evaluation is to compare the use of data from smartphones as well as the combination of data from smartphones and smartwatches for activity recognition.We evidenced that the use of smartphone and smartwatch data combined can increase the accuracy of activity recognition.

Medical Cyber-Physical Systems (MCPS) integrate the cyber space and physical world elements for promoting support for health assurance activities.MCPS are life-critical systems, demanding a strong engineering effort to guarantee safety, what directly impacts on testing process.Testing MCPS using real patients is very expensive and complex, since their lives are involved.Thus, the use of patient synthetic data becomes a promising approach.In this paper we propose a model for improving accuracy of patient synthetic data for testing MCPS based on regression models.We use an existing Patient Baseline Model to generate vital signs of patients, but improving the statistical analysis.Using our approach we increased in about 73.9% the quality of the regression models and, consequently, their accuracies.

One of the key challenges in constructing a Bayesian network BN is defining the node probability tables (NPT).For large-scale BN, learning NPT through domain experts knowledge elicitation is unfeasible.Previous works proposed solutions to this problem using the concept of ranked nodes; however, they have limited modeling capabilities or rely on BN experts to apply them, reducing their applicability.In this paper, we present an expert system based on production rules to define NPTs with the purpose of enabling the definition of NPTs by experts with no ranked nodes-specific knowledge.To create the rules, we elicited data from an expert in ranked nodes.To validate our approach, we executed an experiment with a BN already published in the literature to verify if, with our approach, a practitioner can achieve the same or better configuration for the NPTs.We used the Brier score to assess the NPTs accuracy and evaluated the results with the Wilcoxon test.All the Wilcoxon tests executed rejected the null hypotheses that stated that the Brier scores for the original NPTs method were the same as the new NPTs.By using our solution, a practitioner can accurately define NPTs without understanding the concept of ranked nodes.

Managing software development projects is a complex task because it requires organizing and monitoring several activities.Recently, in order to assist on software projects management, artifact-based models were proposed in the literature.However, the current solutions do not present means to monitor projects health and assist on decisions making.Due to the recent popularization of agile methods, they are the units of study of this research.In this work, we present a method to build artifact and measurementbased models to assess agile projects health.We applied the method to build a generic model based on industrys best practice.We defined the models artifacts and metrics based on findings of a literature review and the assistance of an expert.For each models artifact, we applied the Goal-Question-Metric paradigm to define the metrics.Afterwards, from the GQM meta-model, we constructed a Bayesian network.We validated the model with simulated scenarios.Given the successful results, we concluded that the method and model are promising.

This paper shows the building of an embedded Asterisk platform as a key technique for providing Information Technology classes with an extra laboratorial environment for learning Voice over Internet Protocol. Besides building the platform, the students are asked to mount a functional telephony system with it that meets the criteria of low cost, low building complexity and high adaptability to convergent networks. The Raspberry PI version B was the board used to provide portability for the students to work outside the laboratory environment. Asterisk was presented as a useful tool on which the students could create a flexible system that could be easily deployed on convergent networks. This work was developed in the Telephony subject for the Electrical Engineering and the Telecommunications Systems undergraduate courses from the Federal Institute of Education, Science and Technology of Paraiba – IFPB, and presented good results in the students´ learning achievements.

Electrocardiography (ECG) devices record the heart's electrical activity. These devices can be part of e-Health systems used to diagnose, treat, and monitor patients with cardiovascular diseases. Manufacturers must show to regulatory agencies the effectiveness of devices during the certification process. Nowadays, simulators are used to test and calibrate ECG devices. Besides, manufacturers can use formal methods to specify ECG device requirements to increase dependability. In this context, it is necessary to validate formal models to provide confidence in the specification. Validations can be performed comparing models and physical devices using the same data generated from simulators. However, the simulators available in the market do not enable one to control the amplitude and frequency of signals. This makes difficult the use of the same data into formal models and physical devices. We present in this paper an open and low cost ECG simulator to validate models of ECG devices. The simulator generates ECG signals from digitized characteristic and noise waveforms. We analyzed the quality of the simulator qualitatively using the MATLAB and the Proteus software.

Physicians analyze results obtained from biomedical signal acquisition devices to treat and diagnose patients with acute and chronic diseases. Therefore, results must be accurate to avoid errors during the evaluation of patients. Manufacturers should develop biomedical signal acquisition devices in order to be safe and effective. However, there are still problems related to safety and effectiveness in this type of device according to regulatory agencies such as the US Food and Drug Administration (FDA). In this context, manufacturers should use techniques to perform the formal specification, validation, and simulation to increase the dependability of devices. We propose in this paper a methodology for modeling and simulation of biomedical signal acquisition devices aiming to provide a reliable way to perform tests during its development. A case study of a non-invasive ECG device is presented as a use scenario of the methodology.

Medical devices help physicians to diagnose and treat patients at home and healthcare facilities. However, when complexity of devices grows, new risks can also arise and put users in hazard situations. Manufacturers must develop medical devices taking into account ways to mitigate risks and avoid injuries to users. In this context, regulatory agencies impose the ISO 14971 standard during the risk management of medical devices. The ISO 14971 specifies risk management process activities and suggests manufacturers to use techniques such as the Failure Mode Effects and Criticality Analysis (FMECA). Nevertheless, the ISO 14971 is a subjective document written in natural language. Therefore, the correct interpretation of its requirements depends on the understanding of manufacturers. This paper presents a Colored Petri Nets (CPN) model of the risk management process based on the ISO 14971 and FMECA technique. The model aims to deal with the subjectivity problem of the ISO 14971. Besides, we verified and validated abstract and detailed versions of the model using model checking and simulation results to increase confidence on the specification.

Congestion control algorithms exist for decades to prevent package loss in intense traffic networks. Several solutions developed over the years tried to solve this problem, including the creation of new algorithms. However, hybrid wired and wireless networks, or wired-cum-wireless, common in modern days, bring new challenges to programmers and engineers who try to overcome the numerous new problems that this kind of network presents. This work carries out a performance comparison, in a wired-cum-wireless network, using TCP congestion control algorithms variants: New Reno and CUBIC. The comparison is to determine which of those algorithms performs better in several situations, like multiple disconnections caused by mobility, interference and congested links. The results showed that the CUBIC is a more stable and fair algorithm, but it has a significantly less data flow than New Reno.

Recommendation systems are software tools and techniques that provide customized content to users.The collaborative filtering is one of the most prominent approaches in the recommendation area.Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method.The kNN generates recommendations based on the ratings of the most similar users (nearest neighbors) to the target one.Despite being quite effective, the algorithm performance drops while running on large datasets.We propose a method, called Restricted Space kNN that is based on the restriction of the neighbors search space through a fast and efficient heuristic.The heuristic builds the new search space from the most active users.As a result, we found that using only 15% of the original search space the proposed method generated recommendations almost as accurate as the standard kNN, but with almost 58% less running time.

Recommendation systems have been used in several application domains, most recently for TV (Digital TV, Smart TV, etc.).Several approaches can be used to recommend items, tags, etc., mainly based on user feedback.However, in the Digital TV domain, user feedback has to be done generally by using the remote control, which should be avoided to improve user experience, since assigning explicit feedback to items is restricted by the characteristics of this domain (difficulties when typing with the remote control, etc.).Moreover, in the Smart TV environment several types of items can be recommended (movies, musics, books, etc.).Thus, the recommendation should be generic enough to suit to different content.To solve the problem of acquiring explicit feedback and still generate personalized recommendations to be used by different Smart TV applications, this work proposes a recommendation architecture based on the extraction and classification of terms by analyzing the textual descriptions of TV programs present on electronic programming guides.In order to validate the proposed solution, a prototype using a real dataset has been developed, showing that using the recommended terms it is possible to generate final recommendations for different Smart TV applications.

Most techniques to aid maintenance and evolution of software require to define extension points.Generally, developers try to anticipate the parts that are more likely to evolve, but they can make mistakes and spend money in vain.With Unanticipated Software Evolution, developers can easily change any element of the software, even those that are not related with an extension point.However, we have not found empirical validations of Unanticipated Software Evolution impact on development cost and quality.In this work, we design and execute an experiment for Unanticipated Software Evolution (specifically, using the COMPOR platform), in order to compare its results metrics -time, lines of code, test coverage and complexity --using OO systems as baseline.30 undergraduate students were subjects in this experiment.We concluded that COMPOR have significant impact on the Lines of code and Complexity metrics, reducing the amount of lines changed and the McCabe cyclomatic complexity on evolution of a small system.

Unanticipated Software Evolution (USE) techniques enable developers to easily change any element of the software without being obligated to anticipate and isolate extension points. However, we have not found empirical validations of the impact of USE on development cost and quality. In this work, we design and execute an experiment for USE, in order to compare its resulting metrics — time, lines of code, test coverage and complexity — using OO systems as baseline. 30 undergraduate students were subjects in this experiment. The results suggest that USE has significant impact on the lines of code and complexity metrics, reducing the amount of lines changed and the McCabe cyclomatic complexity on software evolution.

Personal Health Devices (PHDs) with wireless technologies are becoming popular for remotely monitoring patients. However, although these devices are portable and mostly used at home, their integration with Consumer Electronics (CE) devices and networks is still in the earlier steps. This article presents a reference architecture that integrates at home PHDs and CE devices based on the UPnP (Universal Plug and Play) technology and the IEEE 11073 set of standards. This article introduces a UPnP device architecture for personal m-Health (mobile Health), describing how different types of devices interact to exchange personal health information. One of the main features of this work is the use of widely adopted data formats, such as eXtended Markup Language (XML) for health information exchange based on IEEE 11073 data model. Such approach enables web based CE networks, such as UPnP, to interact with PHD devices efficiently.

This paper shows the use of PBL (Problem-Based Learning) technique as a key to learning VoIP in courses like Electrical Engineering and Computer Networks in conjunction with open source and the public domain software called Asterisk which was used to create the scenario of the experiment and the problems presented to the students. In order to make the validation, the experiment was applied to students of Bachelor in Electrical Engineering and in Communication Technology System, undergraduate courses at the Federal Institute of Education, Science and Technology of Paraiba – IFPB, in the Telephony subject, with promising results. The Asterisk software was presented as a useful and flexible tool for constructing scenarios and problems for the teaching of VoIP technologies and the used approach resulted as effective for improving the attainment of the defined learning objectives.

The integration of field level and higher communication is expanding and ensuring ideal conditions for open networks in process automation at industry. For a better knowledge on the referred networks, some experiments were developed to ensure a better comprehension on how they work and application possibilities. The experimental tests with industrial network using a PLC (Programmable Logic Controller) based computer education, allows the electrical engineering students perform experiments on-line by a remote laboratory for the study of industrial automation process.

The increasing availability of connected Personal Health Devices (PHDs) enables a new type of information to be available in the Internet: health information. Most of these devices have specific ways to connect and share information to the Internet through gateways or health managers, creating vertical solutions where one device just talks to one health service. In this context, this paper proposes an architecture that considers the use of different types of health managers and gateways, but keeping interoperability by the use of widely adopted standards. The main contribution of this work is the distribution of health managers in different locations, such as mobile devices and cloud applications, enabling the use of a single health service for different types of PHDs. The ISO/IEEE 11073 standard is used as core technology, enabling the transport of PHD information over different technologies and protocols. We also present a new classification of health managers based on requirements of legacy m-health services. In conclusion, the results of the integration with a real cloud-based connected health system are presented and evaluated.

Electrocardiography devices are used to perform biomedicai signal acquisition from the cardiac electrical activity. Regulatory agencies, such as Food and Drug Administration (FDA) on the USA and National Agency for Sanitary Vigilance (ANVISA) in Brazil, classify Electrocardiography devices as medium risk Class 2 devices. Manufacturers must certify medical devices before commercialization according to some requirements. Prescriptive standards and clinical tests are examples of these requirements. However, problems are still common, and can cause injuries or death to patients. FDA recorded 1.210 computer-related recalls between 2006 and 2011, being 90.5 percent of Class 2. In this context, we are concerned with the use of formal methods to increase the confidence on these medical devices. This paper presents a Colored Petri Nets (CPN) model of a surface Electrocardiography device in order to aid manufacturers to increase the confidence during the devices' development and the certification process.

A surface Electrogastrography (EGG) system uses electrodes placed on the abdomen of the patient to verify the gastric myoelectric activity. Physicians use EGG systems to aid the patients' diagnosis with gastric disorders. In this context, the results of the EGG examinations must be accurate to avoid errors on the patient's evaluation. The medical device and the software to control the device must be safe and effective. This means that not only software behaviors must be verified, but also hardware components. Regulatory agencies verify the conformance of medical systems with these requirements. Nowadays, manufactures show this conformance performing activities such as risk management and clinical tests on devices. We present a methodology using Colored Petri Nets (CPN) in the requirements engineering to model software and hardware behaviors. This includes challenges such as which types of electrical components have to be modeled, and the level of abstraction of the models. Besides, a Matlab application is used to show model simulation results from the proposed CPN models.

The reliability on Safety Instrumented Systems (SIS) is critical for the safe operation of many industrial applications. In particular, SIS play an important role in oil and gas processing plants. SIS are responsible not only for the continuous operation of the plant, it also keeps the plant in a safe state, avoiding damages to the environment and minimizing risks to employees. Therefore, the correct behavior of such systems is an important goal to achieve when building industrial plants. Verification and testing of SIS programs is a very hard task to accomplish. This happens mainly for two reasons. First, testing the real system is very expensive and sometimes it may take a huge amount of time, weeks or even months. Second, those systems deal with a huge number of variables. It is not always possible for a human tester to check if all of them are correct when performing tests. Providing an automatic and formal testing approach for such systems is an important contribution for the development of such systems. In this work we introduce a new method for generating test cases for SIS programs running on Programable Logic Controller (PLC). As we did on previous work, ISA 5.2 diagrams are used as specification of the systems, but now we are using a hardware-in-the-loop technique, and the target artifact is a software running on a PLC. To avoid the execution of redundant tests, we introduced a new test case generation algorithm that is based on equivalence classes. Finally, we discussed a study case in which our method is used to detect error, that were introduced on purpose, on a simple system.

Population aging indicates a high prevalence of chronic illnesses, such as Dementia. Dementia is a chronic and incurable disease that affects several areas of brain, including cognitive areas such as memory, attention, language, and problem solving. The effect of these symptoms causes disability and dependency and it is essential to have a continuous monitoring and assistance of the patients. The disease affects not only the people who have it, but also their caregivers and families, leading them to a physical and emotional overload. To overcome this challenge, it is necessary to reduce the need for physical presence of a caregiver, still providing a constant monitoring. In this work, we propose a framework to support the development of applications to monitor people with Dementia based on a pervasive computing infrastructure, using sensors and mobile devices. To validate the proposed framework, we developed a case study focused on dementia caused by Alzheimer's disease.

Real time multimedia content transmission on the Internet is essential for the most current applications such as voice over IP, video conference, games and web TV. The most popular Internet transport protocols - TCP and UDP - do not suffice when one needs to transmit data from these applications. As a consequence, IETF has been working in new transport protocols that enhance the quality of these multimedia applications. Among all these protocols, DCCP (RFC 4340) is the most effective for multimedia content transmission on the Internet. However, DCCP is not effective in scenarios with many receivers nodes and one sender node. Therefore, this work proposes the Mult(Uni)cast DCCP, a DCCP variant that enables the multimedia data transmission from one to various nodes and supporting non-reliable traffic congestion control. The MU-DCCP uses either multicast or unicast flows according to the network support and data sharing among receiver nodes. The obtained results show that the usage of the MU-DCCP significantly reduces the data congestion in the network while improving the application scalability in terms of the number of receiver nodes.

Energy efficiency has become one of the key challenges for a large class of electronic systems. Longer time between battery recharges is highly desirable for battery-powered devices such as mobile phones, digital cameras, Internet tablets, and electronic organizers. Energy efficiency is also important for electronic systems powered from the electric grid since it may reduce power consumption and the cooling requirements. Power savings are possible because electronic systems generally have an idle state, when, for example, the processor can run in a low-power state. Thus, the correct estimation of the workload model plays an essential role in the decision of which and when a power state transition should be performed by the electronic system. This paper introduces a multisize sliding window workload estimation technique for dynamic power management (DPM) in nonstationary environments. This technique reduces both the effects of identification delay and sampling error present in the previous fixed-size sliding window approach. The system is modeled by discrete-time Markov chains and the model offers a rigorous mathematical formulation of the problem and allows one to obtain an excellent trade-off between performance and power consumption.

This paper presents the BRisa UPnP A/V framework. BRisa is a framework that allows users to discover multimedia devices, share, search and render multimedia content over the local networks or remotely through the Internet. It has been developed using UPnP specifications, which makes use of standard Internet protocols and services, such as HTTP, UDP and SOAP. BRisa is composed of three main applications: the BRisa Media Server, the BRisa Media Renderer and the BRisa Control Point, each of them plays a specific role in our entire UPnP solution. In this paper we briefly describe our UPnP implementation and discuss a set of features implemented to share audio/video/image in a computer network. We also explain the application of our approach to resource limited devices, more specifically for the N800 Nokia Internet Tablet.

This paper presents an experimental study of streaming multimedia packets using DCCP transport protocol over 802.11 g networks. Our main focus is to study the behavior of DCCP flows over real-time multimedia applications. The approach taken was to use DCCP flows in the presence of TCP and UDP flows, then analyze the behavior of each protocol, mainly in regards to the congestion control algorithms of both protocols. Furthermore, we also considered end-points mobility requirements, such as hand-off between multiples access points. Needless to say, the DCCP protocol was recently standardized by IETF as an alternative for streaming multimedia flows on computer networks. Before that, developers had to choose between either TCP or UDP as their transport protocol to stream multimedia packets. However, the lack of some features in both cases makes DCCP an interesting alternative for this kind of application. Therefore, the results presented in this paper show that TCP and DCCP protocols can share the network bandwidth without affecting each other. On the other hand, UDP flows can aggressively degrade TCP and DCCP flows due to the absence of any kind of flow control. Although UDP flows reach high bandwidth throughput, it loses a considerable amount of data taking into account limited bandwidth channels, such as 802.11 g networks. Finally, this work also shows that TCP and DCCP can think of loss of packets as network congestion while experimenting hand-offs, thus decreasing their throughput.

Real-time database management systems (RT-DBMS) have the necessary characteristics for providing efficient support to develop applications in which both, data and transactions have temporal constraints. However, in the last decade, new applications were identified and are characterized by a large geographic distribution, high heterogeneity, lack of global control, partial failures and lack of safety. Besides, they need to manage large data volumes with real-time constraints. Scheduling algorithms should consider transactions with soft deadlines and the concurrency control protocols should allow conflicting transactions to execute in parallel. The last ones should be based in their requirements, which are specified through both quality of services functions and performance metrics. In this work, a method to model and develop applications that execute in open and unpredictable environments is proposed. Based on this model, it is possible to perform analysis and simulations of systems, to guide the decision making process and to identify solutions for improving it. For validating the model, a case study considering the application domain of sensors network is discussed.

In order to provide the news demands that a real-time database needs to work properly, some improvements have been proposed in aspects well done like concurrency control, scheduling and query language. In the perspective of obtain better results, despite the accomplished efforts, these problems continuous been studied. In this paper we introduce a new query language for real-time database (QL-RTDB), that's an extension of the structured query language (SQL-99). Besides the new functionalities of the extended language presented, we also show its implementation with the applied results obtained.

In this work, the problems and respective solutions for dealing with two of the most basic questions regarding handoff management are presented: to avoid total loss of connectivity; and to reduce overall time of the handoff procedure. The focus of this research is on handoff in WPANs for real-time applications. Therefore, Bluetooth stands out as the ideal technology for case study due to its particular alignment with the behavior and requirements of the WPAN’s network model. Bluetooth’s inquiry and paging operations may be calibrated for achieving fast discoveries and connections while scatternets may be used to avoid total loss of connectivity. However, the usage of scatternets and calibration of inquiry and paging unveil a series of challenging aspects when applied along with real-time applications. Such challenges are the priority of inquiry and paging data traffic over users’ applications and unpredictability of scatternet behavior. The key point to overwhelm these difficulties is to understand how they affect the real-time data transfers. In this article a set of discussions and experiments that demonstrate how inquiries, pagings and scatternets can aid achieving the goals of fast and uninterrupted handoffs, as well as the tenuous traps and tradeoffs behind such operations and configurations over real-time applications performance are presented.

In a wireless sensor network (WSN), nodes are power constrained. As a consequence, protocols must be energy efficient to prolong the network lifetime. Network simulators are usually used to develop and analyze WSN protocols. However, most simulators employ the linear battery model to simplify the simulation process, leading to wrong conclusions about the network lifetime, because this model does not take into account the battery capacity recovery effect. In this work we consider a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model) when applying the dynamic power management with scheduled switching modes (DPM-SSM) approach to a blind flooding protocol (i.e., FLOOD). The battery model is implemented in the network simulator NS2, and the FLOOD protocol is modified to use the DPM-SSM technique. Extensive simulation results show that the FLOOD protocol can still achieve good performance results while saving up to 30% on energy consumption.

Safety Instrumented Systems (SIS) are usually designed to prevent accidents, avoid undesirable situations and guarantee continuous operation of oil and gas production systems. An interruption in the operation can be caused by faults in sensors and/or actuators. Hence, SIS are usu ally integrated to the supervisory control system in order to use the information from sensors to prevent such unde sirable situations. In this scenario, it is important to be able to validate the SIS implementation against its spec ification in order to increase the reliability of the system. In this work a technique to improve the dependability of SIS is introduced. A method to obtain a timed automata from a ISA 5.2 specification is presented and applied to a case study provided by Petrobras (Brazilian oil company). Finally, an approach to perform automatic testing of the implementation using the generated model is discussed. The method introduced here is based on the use of the Up paal model checker and the Uppaal-TRON testing tool.

A wireless sensor network (WSN) comprises many sensor nodes each one containing a processing unit, one or more sensors, a power unit, and a radio for data communication. Nodes are power constrained, because they run on batteries, that in many cases cannot be easily replaced. This paper presents a novel dynamic power management technique, named Dynamic Power Management with Scheduled Switching Modes (DPM-SSM), derived from a more realistic analysis of the battery capacity recovery effect and the switching energy. This was only possible thanks to the application of a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model). Preliminary results showed the potential for improving the battery lifetime by taking advantage of the battery recovery effect when a node transitions to a sleeping state, and mostly when transitions are scheduled after packet transmissions. DPM-SSM provides several DPM modes which are triggered depending on the battery remaining capacity. Simulations results show that DPM-SSM can provide real battery power recovery without compromising the timeliness of the applications running on the wireless sensor network.

Pervasive computing has gained much attention from the research community due to the possibility of deploying the first pervasive environments. Therefore, many software solutions are emerging, with the intent of facilitating the development of pervasive applications. Within this scope, in this paper, we introduce a service oriented middleware for pervasive computing, enhanced with runtime flexibility, extensibility for applications, and heterogeneous service provision. Our goal is to enable the middleware and its applications to be adapted to changing operational scenarios. Furthermore, different protocols can be used to discover and access services

It is clear that there is a need to promote the reuse in software development. In the case of real-time embedded system, component based software development can promote such reuse. In this paper we introduce the design of a framework based on components to develop embedded real-time control and automation systems. The basis platforms are the Linux/RT operating system and the embedded system TINI (tiny Internet interfaces) from Dallas semiconductor. The TINI is used as an embedded interface to an automation and control intelligent sensor network application and the Linux/RT is used to implement the real-time server front-end. The Java programming language and design patterns are used to develop the framework.

In the last years it has been observed that real-time database systems (RTDB) are a key functional unit for the success of emergent applications with timing restrictions, such as electronic commerce, sensor networks, telephony systems and online trading. Due to the high service demand, many transactions may miss their deadlines. In the attempt to solve such problem, we present a RTDB model considering quality of service (QoS) to guarantee the performance. A simulation study, presented in this paper, shows that our model can achieve a significant improvement, in terms of deadlines miss and accumulated maximum imprecision resultant of the negotiation between the logical and temporal consistency. Furthermore, we show model analysis generated by the Design/CPN tool.

Recent research points to the real-time database systems (RTDB) as an integrant part in relation to applications as telephony, aviation and stock exchange online. Since for these activities it is not enough to keep the logical consistency of data and transaction, it is necessary to know the imposed temporal constraint to them. However, when some transactions are executed in concurrency way, the consistency of data and transactions cannot be guaranteed. Then, it is necessary that the system controls the interaction between them. The system that controls this interaction is called seriliazibility. In the RTDBs the serializibility is generalized to allow imprecision limited in the transaction processing. This imprecision is defined and controlled through the compatibility function (CF) aiming at allowing a large number of concurrent executions of the transactions. In this research we show a new approach for the CF. Thus, we insert quality of services (QoS) mechanisms in which these make possible that some parameters are limited to acceptable values in order to attend application intention.

We deal with the analysis of semantic concurrency control as well as the scheduling of periodic transactions with hard deadlines for real-time databases (RTBD). A coloured Petri net based method as well as analytical techniques are adopted. The resulting model allows the behavioral analysis taking into account the semantic concurrency control for an effective RTDB application. Also, we define the valid schedules satisfying data and periodic transaction logical and temporal restrictions.

Discusses how to apply P-timed place/transition Petri nets to simulate the continuous dynamics of batch production systems (BPS). Such systems can be seen as a hybrid system since the automation of batch production considers discrete and continuous models. An algorithm to verify the capacity of storage elements in a BPS in a given time interval is introduced. Also, an extension of the introduced algorithm in order to verify overflows are presented.

In this paper we discuss relevant issues of inheritance in the context of object-oriented specification languages with concurrent semantics based on High-Level Petri Nets. A specific formalization, named OO-CPN, is presented to discuss possible approaches to deal with inheritance in such a context.

This paper focus on results related to the design of a model of a Computer-based Interactive Learning Environment oriented to the Petri net domain, named PN-M. This environment adopt a a multi-agent approach based on a view about the domain knowledge structure as defined in the MATHEMA interactive learning environment. PN-M is focused on problem solving. Hence in this paper we introduce the domain of the problem, that is the design of interlocking Petri models for track vehicle systems.

Groupware systems are inherently concurrent and complex. To formally specify such systems, designers need powerful description tools capable of dealing with concurrency and mechanisms to manage the complexity. In this paper, we present an object-oriented specification language based on Petri nets, and its application to the modeling of a cooperative editor.

This paper introduces extensions in an Object Coloured Petri Net model named G-CPN to model a real-time control and database system. These extensions allow the designer to apply an unified approach to model the control objects and the database objects of a real-time system. Syntactic and semantical constraints are considered for real-time database objects. The semantical constraints can be mapped to Petri net constructions. An example based on a manufacturing cell is shown.

When specifying, designing and analyzing complex real-time systems, it is necessary to adopt a modular or compositional methodology. This methodology shall allow the designer the ability to verify local properties of individual modules or components in the system, and also shall allow the verification of the correct behavior of interacting components. The application of Petri nets for the modeling and verification of systems, at specification and design levels are well known. Despite the powerful structuring mechanisms available in the Petri nets theory for the construction of the model of complex systems, the designer is still likely to face the problem of state explosion, when analyzing and verifying large systems. In this work we introduce a modular analysis methodology for a kind of high level Petri nets named G-Nets.