Requirements Technical Debt (RTD) emerges when software requirements are elicited, documented, or implemented in a way that deviates from stakeholders’ needs or quality standards. Despite its growing recognition as a key dimension of Technical Debt, there is a lack of comprehensive and empirically validated processes for RTD management. This paper presents an experience report on the proposal and evaluation of the MANA-RTD process, a structured approach to manage Requirements Technical Debt throughout its lifecycle, integrating the process into an existing management platform, named DTRUST. The goal is to improve the traceability, prioritization, and repayment of RTDs in real software projects. Supported by a literature review, the MANA-RTD process was proposed, and a case study was conducted in a government software project. The process was assessed using the SWOT technique and the Goal-Question-Metric (GQM) approach to analyze its usefulness and suitability. The application of MANA-RTD resulted in more systematic RTD management, with 50% of identified debts fully repaid, and improved documentation and monitoring practices. The results highlight the benefits of integrating structured RTD management into existing platforms, thereby enhancing visibility and decision-making. The MANA-RTD process demonstrated practical value and methodological rigor for managing RTD in real-world contexts, providing a foundation for future extensions and replications in other domains.

The Human-Computer Interaction community worked over the last years on scoping and defining User Experience (UX). As reported in the literature, UX’s understanding changes according to the socio-cultural context and between professional positions, making a global consensus unattainable. Nevertheless, tracking and investigating the most accepted UX definitions and statements may be advantageous for visualizing multiple contexts’ needs. Such an investigation is still scarce and even more significant in developing countries since there are structural differences from developed countries in the software development industry that directly impact user research practices. To fill in this gap, we replicated in Brazil a survey distributed before, mostly among professionals from developed countries. We compared 252 responses from academics and practitioners and identified differences and similarities among Brazilian and international communities. The definitions of UX chosen by the academic and industrial domains explain differences in UX design and evaluation. When comparing Brazilians’ responses with previous studies, Brazilians give greater importance to qualitative measures and to different UX time spans.

Having a full and healthy life is what most people want. This involves a set of domains such as physical and psychological health, social relationships, and environmental issues. Together, these domains can be used to characterize a person's Quality of Life (QoL). This desire for a healthy life becomes even more prominent as the world population ages. Thereby, new socioeconomic challenges arise, putting pressure on governments and industries to adopt technologies to improve QoL. One of the technologies that have been widely used is the Internet of Health Things (IoHT). However, developers still face challenges when proposing IoHT solutions, such as lack of interoperability, high volatility, difficulties in providing runtime adaptations, high development cost when it involves AI, and the absence of a semantic model for QoL data. Thus, this Ph.D. project aims to investigate the development of QoL-based IoHT systems to address these challenges. As a result, an IoT-Health Platform is expected to support this development and bring health professionals closer to this process.

The advance of Ubiquitous Computing (UbiComp) and Internet of Things (IoT) brought a new set of Non-Functional Requirements (NFRs), especially related to Human-Computer Interaction (HCI). Invisibility is one of these NFRs, and it refers to either the merging of technology in the user environment or the decrease of the interaction workload. This new NFR may impact traditional NFRs (e.g., Usability), revealing positive correlations, when one NFR helps another, and negative correlations, when a procedure favors an NFR but creates difficulty for another one. Software engineers need to know about these correlations, so they can select appropriate strategies to satisfy Invisibility and traditional NFRs. Correlations between NFRs are usually stored in catalogs, which is a well-defined body of knowledge gathered from previous experience. Although Invisibility has been recently cataloged with development strategies, the literature still lacks catalogs with correlations for this NFR. Therefore, this work aims at capturing and cataloging invisibility correlations for UbiComp and IoT systems. To do that, we also propose to systematize the definition of correlations using the following well-defined research methods: Interview, Content Analysis and Questionnaire. As a result, we defined a catalog with 110 positive and negative correlations with 9 NFRs. This well-defined body of knowledge is useful for supporting software engineers to select strategies to satisfy Invisibility and other NFRs related to user interaction.

Software engineers are traditionally concerned with performance and capabilities of mobile applications, while Human-Computer Interaction (HCI) approaches remain underused in many cases of industry everyday practice, especially in smaller companies, running on a tight schedule.

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.

International audience

The development of systems for the Internet of Things (IoT) environment is challenging, because it deals with features such as self-adaptability and the dynamism of the environment. Furthermore, the traditional development approach based on design-time decisions is inadequate if we consider the number of devices and technologies involved, and the inability to treat unforeseen scenarios at development time. Therefore, the development of IoT systems requires the use of practices that allow adapting the behavior and structure of the system in a more flexible way. Approaches premissed on models and structures that describe behavior, such as policies and workflows, have been proposed in the literature to achieve self-adaptation. However, the existing approaches do not fully support the main requirements of an IoT environment or are coupled to the environment and technologies. Thus, we propose a decoupled support mechanism, called SUCCEEd, that allows the creation and execution of a sequence of actions in an IoT environment. We have evaluated our mechanism regarding coupling and performance. The results show that: (i) our mechanism is a good option for reuse once is decoupled from middleware platforms; (ii) the time used to adapt is less than the time used to perform functionalities; and (iii) the number of steps needed to adapt is minimal in a matter of time

Fog and Mist computing exploits computational resources of IoT devices located at the edge of the network. This new infrastructure brings benefits related to latency and Internet bandwidth utilization when compared with Cloud computing. However, this scenario is quite challenging mainly due to the considerable heterogeneity of the devices and to the dynamicity of the network topology what adds new issues related to security, privacy, data availability, and service availability, among others. Despite the theme of data dissemination and data storage had been relatively explored in fields of Sensor Wireless Network (WSN) and Vehicular Ad Hoc Networks (VANETs), we found few studies in the Fog computing research area. Then, this work focuses on the problem of data availability and dissemination in this environment by proposing a distributed algorithm using ideas from evolutionary computation and epidemic models. Results obtained through simulation suggest that the algorithm presents proper results in maintaining data availability in the Fog and Mist Computing environments, even under severe conditions.

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.

Internet of Things arises as a computational paradigm that promotes the interconnection of objects to the Internet and enables interaction, operational efficiency, and communication. With the increasing inclusion in the network of intelligent objects that have characteristics such as diversity, heterogeneity, mobility and low computational power, it is fundamental to develop mechanisms that allow management and control. In addition, it is important to identify whether the assets are working properly or have anomalies. Traffic classification techniques are important to aid in network analysis and to handle many other key aspects such as security, management, access control, provisioning, and resource allocation. In order to promote the identification of network devices, especially IoT, this article presents a technique that uses Random Forest, a supervised automatic learning algorithm, together with the inspection of the contents of the packages for this purpose. Also, we use the same algorithm to perform the classification of network traffic. In the end, the identification of the devices showed an accuracy of approximately 99%.

Conflicts between non-functional requirements (NFRs) can happen when a particular solution favors the first NFR but creates difficulty for the second one. Usually, in the literature, the conflict identification uses catalogs to indicate conflicting NFRs. The authors focus on NFRs that are generic to any system. However, applications from Ubiquitous Computing (UbiComp) and Internet of Things (IoT) introduce new kind of NFRs (e.g., Context-Awareness and Mobility). They are not included in these catalogs and can impact the traditional ones (e.g., Security and Usability). Therefore, there is a need to create a catalog that considers conflicts among quality characteristics for UbiComp and IoT applications. However, the literature lacks a process or guideline about how to create a catalog. This paper aims then to present a process and initial findings towards a catalog of conflicts between specific HCI quality characteristics in UbiComp and IoT applications and Usability.

A new set of Non-Functional Requirements (NFRs) have appeared with the advent of Ubiquitous Computing (UbiComp) and more recently Internet of Things (IoT). Invisibility is one of these NFRs that means the ability to hide technology from users. Although invisibility is long seen as an essential characteristic for achieving the goals of UbiComp, it has not been cataloged regarding its subcharacteristics and solutions, making its design and requirements specification in such applications a challenging task. Considering the Softgoal Interdependency Graph (SIG), which is a well-known format to catalog NFRs, this work aims at capturing subcharacteristics and solutions for Invisibility and cataloging them in a SIG. Since there is no systematic approach on how to build SIGs, we also propose to systematize the definition of Invisibility SIG using the following well-defined research methods: snowballing, database search, grounded theory and questionnaires. As a result, we got an Invisibility SIG composed of two main subcharacteristics, twelve sub-subcharacteristics, ten general solutions and fifty-six specific solutions. This organized body of knowledge is useful for supporting software engineers to specify requirements and practical solutions for UbiComp and IoT applications. Furthermore, the proposed methodology used to capture and catalog requirements in a SIG can be reused for other NFRs.

A cloud storage service implements security mechanisms to protect users' data. Moreover, due to the loss of control over the cloud infrastructure, it is essential to demonstrate its security, increasing the trust and transparency in cloud services. However, an analysis of the literature reveals flaws in existing solutions that do not identify all violations. Then, a secure storage service for cloud computing is proposed to address these issues, combining different security mechanisms. The proposed solution includes auditing and monitoring mechanisms to detect and prove violations of security properties. Moreover, Colored Petri Nets (CPNs) are used for evaluation. As results, the violation detection is improved, and the provider cannot deny the identified violations.

In the Internet of Things paradigm, the total number of interconnected smart objects tends to increase in elevate rates in the next years, creating an overload of objects to be controlled and configured by the users themselves. Alternatively, context-aware discovery services have potential to minimize this problem by applying rules to determine which smart objects are more suitable for users, at a certain time. To help acquiring the contextual information, there are several context acquisition middlewares, e.g., LoCCAM, that creates abstraction layers which provides application a simple way to define and apply contextual discovery rules. This work proposes the CoAP-CTX, an extension of the built-in discovery service present in the CoAP protocol. Our approach aims to provide support to a context-aware discovery service of smart objects. The CoAP-CTX matches users' interest and context with the smart objects state data to retrieve most relevant smart objects to the user in this instant. Additionally, smart objects that are not of the user's interest go into an idle state, optimizing the network and battery usage. Experiments have shown that CoAP-CTX can decrease the total number of messages up to 80% in scenarios where a large number smart objects are presents in a generic environment, like working buildings. The overhead of the discovery latency shows a maximum discovery time increase of 1.5 seconds.

Simulation is the most frequently adopted approach for evaluating protocols and algorithms for Vehicular Ad hoc Networks (VANETs) and Delay-Tolerant Networks (DTNs). Usually, simulation tools use mobility traces to build the network topology based on the existing contacts between mobile nodes. However, quality of the traces, in terms of spatial and temporal granularity of each entry in the logfile, is a key factor that impacts the network topology directly. Therefore, the reliability of the results depends strongly on the accurate representation of the real network topology by the vehicular mobility model. We show that five widely adopted existing real vehicular mobility traces present gaps, leading to fallible outcomes. In this work, we propose a solution to fill those gaps, leading to more fine-grained traces, which lead to more trustworthy simulation results. We propose and evaluate a data-based solution using clustering algorithms to fill the gaps of real-world traces. In addition, we also present the evaluation results that compare the communication graph of the original and the calibrated traces using network metrics. The results reveal that the gaps do indeed induce network topologies differing from reality, decreasing the quality of the evaluation results. To contribute to the research community, we have made the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.

Vehicular network applications are emerging to bring many benefits to the users during their journey. However, the design and deployment of such applications require studies that provide insightful information about the network formed by vehicles. To this end, in the last years researchers have been characterizing real mobility traces collected from taxis equipped with GPS devices. However, these traces present temporal and spatial gaps, as it was demonstrated in other studies. In this work, we compare the real original traces with their calibrated version (i.e., with no gaps) to show that the existing gaps expressively affect key metrics such as contact duration, inter-contact time, and network capacity. As result, we contribute to the research community by presenting important analysis of real vehicular network topology and by showing the importance of calibrating the traces before evaluating vehicular network applications.

The vast diffusion of devices equipped with a GPS receiver has brought the possibility of collecting data related to massive amounts of moving objects on a scale never seen before. During the latest years, such diffusion instigated the development of many different techniques to deal with location prediction problems. Existing works mainly aim at predicting the next location of moving objects by focusing on information in the spatial domain. In this paper we want to take into account information in the temporal domain as well, both to improve the reliability of predictions and to answer not only where a moving object is going to move, but also when an object is expected to leave its current location.

For Dynamic Software Product Lines (DSPLs), evaluating the quality of a feature model is important to ensure that errors in the early stages do not spread throughout the DSPL. Measures extracted from feature models have been proved to be useful in the quality evaluation of such models. However, the process used for computing the values of these quality measures for a large set of feature models can be cumbersome and error prone. To cope with this problem, we present DyMMer, a tool to support the automatic extraction of quality measures from feature models in DSPLs. After that, we can analyse the results and propose improvements for the feature models. Currently, the DyMMer tool is able to collect 40 different quality measures from a DSPL feature model.

As the number of interacting devices and the complexity of cyber-physical systems increases, self-adaptation is a natural solution to address challenges faced by software developers. To provide a systematic and unified solution to support the development and execution of cyber-physical systems, this doctoral thesis proposes the creation of an environment that offers mechanisms to facilitate the technology-independent communication and uncoupled interoperable coordination between interacting entities of the system, as well as the flexible and adaptable execution of the functionalities specified for each application. The outcome is a set of modules to help developers to face the challenges of cyber-physical systems.

In the ecosystem of Internet of Things, many applications require external computational resources with quick response. In this context, a possible solution is to use computational resources from other devices that are located nearby the host device. This new environment is named Fog computing and creates several opportunities and challenges. One these challenges, which is the focus of this work, is how to allocate available resources in a simple and effective way for a client device that you want to use these features. The proposal "Smart Shadow" is an autonomous platform availability allocation of computer resources in a Fog computing infrastructure based on learning from previous event to support mobile hosts devices.

Information technology (IT) services have become more complex and diversified. The service desk establishes a single channel of communication between the internal IT provider and its users, through which incidents and problems are analyzed, escalated and resolved. Managers need to analyze different operating scenarios regarding service desk capacity to make effective decisions about what should be improved to meet the business demands. This paper presents a system dynamics model for evaluating an organization's service desk's service capacity. The model allows the performance simulation of scenarios, estimating the service capacity. Preliminary results are promising and seem to contribute to the improvement of capacity management processes.

The widespread use of mobile devices is reshaping the way users interact, allowing applications to explore physical proximity of devices to offer intelligent services. Although there are several communication technologies available (e.g., Wi- Fi, Wi-Fi Direct, LTE, UMTS, Bluetooth, NFC), building an application from scratch considering both communication and application requirements is not a trivial task. In order to help developers to implement communication primitives, this paper presents a communication framework for ubiquitous systems called USABle. Two important contributions of this framework can be highlighted: i) a set of technology-independent interfaces to handle communication requirements; and ii) a modular and extensible layer-based architecture that permits developers to implement new communication technologies and message dissemination strategies. The outcome of using this communication framework is a simpler way to build applications that explore physical proximity. To validate the claims presented in this paper, a performance evaluation using real devices is done, analyzing two multi-hop and three carry-and-forward implementations of routing algorithms that were implemented using the USABle framework.

Geology is a fundamentally important natural science that explores resources such as minerals, water, fossil fuels and many others. Geology's field of knowledge also has social relevance in preventing natural disasters. Because of these features, geology is important not only as a science itself but in several other areas, such as engineering, biology, and oceanography. In this context of a natural science, the field trips are an essential activity for learning. During these classes, students need to make notes, take photos and perfom measurements using different equipment, and pay attention to the teacher's instructions. This paper presents the specification, development and use of a mobile application to enhance and aid in geology field trips called Geomóvel. A case study was performed that exhibited good acceptance of the application by the students, who stated that the major benefits were ease of use and practicality of annotation in multiple formats. The application also enables data to be exported to Google Earth such that the teacher can analyze the data recorded in the case study.

Context-awareness has been successfully included in the mobile device applications due mainly to the presence of numerous sensors and the access to several communication networks. Therefore, this paper presents a context-aware framework based on geolocation for a health care governance system. While enabling remote health diagnosis of a patient using his/her own mobile device, the proposed system enriches the patient's health data with geolocation before sending it to the Lariisa framework, an intelligent platform to support decision-making in public health governance. In order to collect information about a patient's health status, the proposed application uses mobile devices equipped with medical sensors. While remotely registering a patient's health diagnosis using a mobile device, the system allows the creation of an enriched description of the user context (e.g. weather, location, and date). The main contributions related to our framework include patient health diagnosis provided remotely, support for decision-making health systems, and context information for context-aware health systems.

The evolution of mobile technologies allows the emerging of ubiquitous systems, able to anticipate user needs and to seamlessly adapt to context changes. These systems present the problem of dynamic adaptation in a highly distributed, heterogeneous and volatile environment, since it may be difficult to collect and process context information from distributed unknown sources. In order to facilitate the development of such systems, this paper extends an existing coordination framework based on tuple spaces, aiming at the management of distributed information. Hence, a decentralized coordination framework was created, offering primitives to developers to create ubiquitous systems able to interact and cooperate in scenarios of total decentralization. This paper reports some experimental results obtained in a testbed of smartphones and tablets which demonstrate the practical feasibility of our approach and pointed out how our solution can grant context data dissemination in ad hoc and infrastructured networks.

Vehicular Ad hoc NETworks (VANETs) have been widely used to provide data exchange services in Intelligent Transport Systems (ITS), which should facilitate user's routines. At the same time that user's routines are improved, they can also be used as an important source of data for helping the decision-making process of VANET services. These routines can be acquired from vehicle's trajectories through the use of Global Positioning Systems (GPS). In this context, the combination of location-based data and data forwarding methods brings interesting challenges since these topics are not directly related. Therefore, the key idea of this work is to provide a geocast routing mechanism in VANETs based on daily movements of users in their vehicles. The main contribution is related to the delivery rate, which increases in partitioned and sparsely connected networks. The results show the efficiency of our new method to perform opportunistic routing based on location data, which discovers the best routes to forward packets through the network.

Software Product Lines (SPLs) have evolved and gained attention as one of the most promising approaches for software reuse. Feature models are the main technique to represent domain variability in SPLs. However, there are other domain aspects, besides variability, which cannot be expressed in a feature model. Also, these diagrams were not designed to facilitate information retrieval, interoperability and inference. In contrast, ontologies seem to be the best solution to meet these requirements. Therefore, this work presents an approach for semantic enrichment of SPLs using ontologies. Our proposal provides methods to add domain information besides variability description, and a top-ontology that specifies generic concepts and relations in an SPL, working as a guide model for information addition. The proposed approach reuses the existing SPL feature model, adding semantic descriptions in a less intrusive way than modifying the feature model notation.

Designing routing protocols in vehicular network is a challenging task due to the high mobility of nodes, the instability of wireless links, and the diversity of deployment scenarios. Several routing protocols have been designed with the goal of solving one or more specific problems of each scenario. In this paper, we present a performance evaluation of routing protocols in vehicular networks addressing the impact of density, load, and mobility pattern. Four protocols are evaluated in urban and highway scenarios. The results presented in this paper provide guidelines for network designers in planning efficient routing protocols for vehicular network, able to adapt to different scenarios.

SUMMARY Complex scientific experiments have a growing demand for computational resources, which are expensive to be acquired and maintained. Grid computing has emerged as the mainstream technology to solve this issue. Grids are also adequate for the execution of scientific workflows because they allow the use of heterogeneous and distributed resources. In spite of the progress in grid technology, there are challenges to overcome in workflow scheduling. For instance, centralized scheduling solutions may lead to performance degradation and to problems with scalability. Some scheduling approaches are partially distributed, keeping a few centralized components that may become bottlenecks. Other distributed solutions have a lack of flexibility in the definition of workflows, in which only the use of tasks as steps in the workflow is permitted not high level services. In this work, we present P2PScheMe, a scheduling mechanism for peer‐to‐peer execution of workflows based on the invocation of grid services. The proposal considers information regarding grid execution environment in order to allow workflow scheduling adaptation. This adaptation is performed according to user requirements of quality of service. In this paper, we describe how P2PScheMe works and provides a comparative analysis with existing solutions. Copyright © 2011 John Wiley & Sons, Ltd.

Learning Objects (LO) are digital resources developed to help teachers present the pedagogical concepts to students. However, the way these resources are produced does not allow teachers to adapt them to the reality of their students. Thus, this paper proposes a development process of customizable learning objects using a strategy based on models. Based on this approach, teachers will make adjustments to the LO and thus achieve more autonomy in the use of these resources.

Abstract Computational grids are characterized by the collaborative work among environment devices. Further, grid applications have been built based on reputation solutions. However, those applications can suffer due to attacks from malicious nodes. These nodes may not only corrupt the result from processed jobs, but also intentionally acquire a good reputation so as to obtain privileges to damage other nodes. In order to detect and isolate these malicious nodes (known as intelligent cheating nodes) from a P2P grid computing, this work proposes a system‐level diagnosis model, using a strategy based on voting and honeypots. The model is evaluated by means of scenarios that take into account different percentages of malicious and cheating nodes. Achieved results show the robustness and efficiency of our diagnosis model, once all cheating nodes can be detected, with an accuracy of 99.4% of jobs being correctly processed. Furthermore, a graphical user interface is implemented for visualizing the simulations. Copyright © 2009 John Wiley & Sons, Ltd.

When dealing with Wireless Sensor Networks (WSN) applications that demand a high level cooperation of the sensor nodes, time synchronization is necessary to provide the correctness required for their results. In this work, we show that common issues between routing and time synchronization processes make their integration possible and, thus, can solve the problems of the resources consumption and time spent to acquire the synchronization. Two proposals for integrating them are presented.

This paper presents a novel programming model for explicitly separating parallelization and middleware-specific concerns in bag-of-tasks (BoT) grid applications. The model uses Java threads for application decomposition, and aspect-oriented programming to dynamically (and transparently) convert the application's thread objects into independent grid tasks, using the services of a specific grid middleware API. This paper also describes early experimental results showing the model's modularity gains and performance overhead, when applied to implement BoT grid applications using OurGrid.

Malicious manipulation of jobs results endangers the efficiency and performance of grid computing applications. The presence of nodes interested in depreciating jobs results may be detected and minimized with the usage of fault tolerance techniques. In order to detect this kind of nodes, this paper presents a distributed and hierarchical diagnosis model based on comparison and reputation, which can be applied to both public and private grids. This strategy defines the status of a node according to its level of confidence, measured through its behavior. The proposed model was submitted to simulations to evaluate its effectiveness under different quota of malicious nodes. The results reveals that 8 test rounds can detect practically all malicious nodes and even with less rounds, the correctness remains high without a significant overhead increase

Refactoring, i.e., the process of changing a software system to improve its internal quality and preserving its external behavior, is gaining increasing acceptance among software developers. Even though many refactoring tools are now available for a variety of programming languages, most of them are difficult to reuse, extend or even customize, mainly because they provide no uniform way of representing and manipulating source code information. This work presents a refactoring framework, called RefaX, which relies on open, XML-based source code models and processing standards to facilitate the development, customization and reuse of refactoring tools. In particular, RefaX makes it possible to develop refactoring tools that are independent of source code model, programming language and XML processing technology. The viability of the framework is illustrated through a refactoring prototype for Java.