User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is essential to train software engineers on how to create simple, easily readable, and comprehensive User Stories. For that reason, we designed, implemented, applied, and evaluated a web application called User Story Tutor (UST). UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement. UST also estimates a User Story effort in Story Points using Machine Learning techniques. As such UST may support the continuing education of agile development teams when writing and reviewing User Stories. UST's ease of use was evaluated by 40 agile practitioners according to the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation averages were good in almost all considered variables. Application of the AttrakDiff evaluation framework produced similar good results. Apparently, UST can be used with good reliability. Applying UST to assist in the construction of User Stories is a viable technique that, at the very least, can be used by agile developments to complement and enhance current User Story creation.

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.

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.

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.

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.

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.

A research work is reproducible when all research artifacts such as as text, data, figure and code are available for independent researchers reproduce the results. In this paper, we present a reproducible gait analysis to track Parkinson's Disease evolution by monitoring walking abnormalities. Weapplied Principal Component Analysis into gait data to detect user's abnormalities that may indicate the progression of Parkinson's Disease. We validated our approach with a public database of foot sensor data, which includes vertical ground reaction force records of subjects with healthy gait and Parkinson's Disease patients. We used the euclidean distance asdata classifier. We reached a classification accuracy of 81.00% with leave-one-out cross-validation, which demonstrates the feasibility of our approach for tracking PD's symptoms based on user gait. All relevant data to reproduce our results are available in a public web page.

Parkinson's disease (PD) is a degenerative neurological disorder. It causes motor symptoms such as resting tremor, bradykinesia and gait disorders. The disease's progressive nature requires continuous monitoring of the motor symptoms to assist the neurologist in managing medication. With this purpose, Health Monitoring Systems (HMS) are used as a decentralized healthcare approach. On the other hand, most patients reject the current HMS solutions because they are invasive and stigmatizing. In this work, we present a non-invasive HMS for PD motor symptoms based on games. Because of the nature of games, the approach is able to collect data from patients without reminding them that they are under a disease's treatment. We validated our approach with 30 research subjects divided between PD group and Control group. We used Support Vector Machine (SVM) to identify the occurrence of PD's bradykinesia motor symptoms and reached a classification precision of 92.31%. Furthermore, 90,00% of the patients approved our HMS considering it as non-invasive and easily integrated into their routine.

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.

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.

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.

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.

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.

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

This paper presents a virtual environment which enables people interested in acquiring and sharing knowledge about Brazilian popular music to congregate in communities. The environment includes a music virtual library and a cooperative learning system based on the harmony trees theory, an innovative harmonic teaching method which has been used successfully by its author for several years.

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.