Cloud computing services consumers do not have enough reliable information about critical characteristics of their providers, such as performance, security, trust and privacy, compliance with laws and regulations, among others. Our proposal addresses these problems presenting a trust assessment framework that integrates three domains: Governance, Transparency, and Security Information. Our approach is consumer-centric and deals with trust aspects from the end-user's perspective. We use Indicators to communicate the outcomes, which aim to represent the expression of cybersecurity, manageability, and transparency of services under assessment. This paper includes an implementation proposal, prototype, and proof of concept, in which the framework was applied in a real scenario and executed over a long-term (18 months) usage simulation to verify its applicability, sensitivity, and robustness. Our study is intended for use by consumers of cloud computing who seek to know and measure levels of cybersecurity, protection of privacy, transparency of security, and high levels of quality in their services and infrastructure.

A public service examination is an effective manner of selecting civil and military servants for admission to various sectors of public service. These examinations usually attract well trained personnel by presenting highly competitive and meritocratic selection. Nevertheless, they also attract criminals that offer candidates the possibility of easy and illegitimate admission. Aiming to provide better security and trust in the selection process, we propose a conceptual method that uses data mining and statistical techniques to detect fraud in public service examinations. The method analyzes geographic and biographical data of candidates, scores, and similarity of answers of one candidate to those of other candidates, as well as past information, to compose a comprehensive index of suspicion. When the value of the index is above a certain limit, it indicates a degree of suspicion of an approved candidate, which may call for further investigation.

Information Security is an aggregation of people effort, processes and technology to help organizations at providing confidentiality, integrity and availability in their information assets. Nowadays, almost all technology services are heavily dependent on cloud computing under their various forms of service models. We realize there are weaknesses in cloud services concerning issues of performance, reliability, security, and privacy, among other issues. Cloud services consumers do not have enough information on these issues neither on compliance with laws and regulations. In this paper, we present a comprehensive mapping of works that deal with cloud trust aspects from user perspective. We aim at identifying how users or consumers of Cloud services measure trust on cloud computing services. Our main contributions are: (i) an updated study on the state of the art of cloud trust models; (ii) a taxonomy of characteristics and contributions to cloud trust; (iii) a discussion on works which present hard problems of research on cloud trust models and gaps of contributions in the literature.

Nowadays, military cyber-missions are increasingly complex, demanding specific and critical socio-technical operational resources. We propose a conceptual proposal of a system architecture aimed at supporting complex operations of cyber-missions. The architecture allows the systematic selecting and prioritizing socio-technical resources for cyber-missions in a systematic manner. The foundations of the proposed architecture are based on a mission operations ontology; it was developed for formalizing the main concepts. The main contributions of the work are: (i) Conceptual Architecture to supporting complex cyber-mission operations; (ii) Lightweight ontology to make explicit its main concepts; and, (iii) an Application Scenario of the system architecture. Our proposal is meant to be useful for researchers, developers and actuators who deal with socio-technical systems in cyber-mission operations.

Many traceability models lack well-defined traceability link types, provide incomplete coverage of situations, do not provide mechanisms to ensure consistency of traceability, and consider only requirements traceability ignoring other activities of development. We propose a set of basic concepts for traceability, a reference model, and a comprehensive metamodel for traceability created using this reference model. The reference model defines: basic elements for traceability, basic actions to be done on artifacts, basic properties that sets of link types and artifact types should have, basic categories that should be realized regarding these sets, and basic set of processes for traceability. The metamodel is composed of a visual model defining how its elements interact, the definition and semantic description of link types and artifact types which realize the categories of the reference model, and a set of detailed processes describing the steps to maintain traceability and system consistency. Our proposal aims to reduce the problems identified; the reference model provides directions to help the creation, or evaluation, of a traceability model; the metamodel provides semantically described traceability link types, coverage of the most common situations, mechanisms to ensure consistency of traceability, and covers the most common activities in software development.

Information security is a critical issue in the context of complex and interconnected nowadays IT Systems. Innovative testing approaches are demanded to verify whether the main security characteristics are provided in the systems. The conceptual formalization level required by security test processes can be supplied by semantic technologies. STEP-ONE is the Security TEst Process supported by ONtology Environment; its foundations come from largely accepted testing processes and security testing standards. The STPO (Security Testing Process Ontology) formalizes and makes explicit the main concepts of the domain. We present a conceptual characterization of STEP-ONE and describe a usage scenario to illustrate how the proposal can be applied. The main contributions are the conceptual process and the ontology. Our proposal is meant to be applied for the evaluation of IT systems with respect to security characteristics as well as to make systematic the security testing activities.

Summary Nowadays, personal information is collected, stored, and managed through web applications and services. Companies are interested in keeping such information private due to regulation laws and privacy concerns of customers. Furthermore, the reputation of a company can be dependent on privacy protection, ie, the more a company protects the privacy of its customers, the more credibility it gets. This paper proposes an integrated approach that relies on models and design tools to help in the analysis, design, and development of web applications and services with privacy concerns. Using the approach, these applications can be developed consistently with their privacy policies to enforce them, protecting personal information from different sources of privacy violation. The approach is composed of a conceptual model, a reference architecture, and a Unified Modified Language Profile, ie, an extension of the Unified Modified Language for including privacy protection. The idea is to systematize the privacy concepts in the scope of web applications and services, organizing the privacy domain knowledge and providing features and functionalities that must be addressed to protect the privacy of the users in the design and development of web applications. Validation has been performed by analyzing the ability of the approach to model privacy policies from real web applications and by applying it to a simple application example of an online bookstore. Results show that privacy protection can be implemented in a model‐based approach, bringing values for the stakeholders and being an important contribution toward improving the process of designing web applications in the privacy domain.

Malicious code attacks pose a serious threat to the security of information systems, as malware evolved from innocuous conceptual software to advanced and destructive cyber weapons. However, there is still the lack of a comprehensive and useful taxonomy to classify malware according to their behavior, since commonly used names are obsolete and unable to handle the complex and multipurpose currently observed samples. In this article, we present a brief survey on available malware taxonomies, discuss about issues on existing naming schemes and introduce an extensible taxonomy consisting of an initial set of behaviors usually exhibited by malware during an infection. The main goal of our proposed taxonomy is to address the menace of potentially malicious programs based on their observed behaviors, thus aiding in incident response procedures. Finally, we present a case study to evaluate our behavior-centric taxonomy, in which we apply identification patterns extracted from the proposed taxonomy to over 12 thousand known malware samples. The leveraged results show that it is possible to screen malicious programs that exhibit suspicious behaviors, even when they remain undetected by antivirus tools.

The ubiquity of Internet-connected devices motivates attackers to create malicious programs (malware) to exploit users and their systems. Malware detection requires a deep understanding of their possible behaviors, one that is detailed enough to tell apart suspicious programs from benign, legitimate ones. A step to effectively address the malware problem leans toward the development of an ontology. Current efforts are based on an obsolete hierarchy of malware classes that defines a malware family by one single prevalent behavior (e.g., viruses infect other files, worms spread and exploit remote systems autonomously, Trojan horses disguise themselves as benign programs, and so on). In order to address the detection of modern, complex malware families whose infections involve sets of multiple exploit methods, we need an ontology broader enough to deal with these suspicious activities performed on the victim's system. In this paper, we propose a core model for a novel malware ontology that is based on their exhibited behavior, filling a gap in the field.

Malicious programs (malware) cause serious security issues to home users and even to highly secured enterprise systems. The main infection vector currently used by attackers is the Internet. To improve the detection rate and to develop protection mechanisms, it is very important to analyze and study these threats. To this end, several systems were developed to perform malware analysis, which support operating system (OS) programs or Web codes, but they all suffer from limitations. Also, the existing systems focus only on one type of malware, those that target the OS or that require a Web browser. In this article, we propose a framework that is able to analyze Web and OS-based malware, which provides better detection rates and a broader range of malware types analysis. We have also evaluated and compared our analysis results to the state-of-the-art systems, presenting the advantages of the developed framework over them when regarding Web and OS-based malware.

Planning and scheduling of testing activities play an important role for any independent test team that performs tests for different software systems, developed by different development teams. This work studies the application of machine learning tools and variable selection tools to solve the problem of estimating the execution effort of functional tests. An analysis of the test execution process is developed and experiments are performed on two real databases. The main contributions of this paper are the approach of selecting the significant variables for database synthesis and the use of an artificial neural network trained with an asymmetric cost function.

Testing database applications is crucial for ensuring good quality software as undetected faults can result in unrecoverable data corruption. SQL is the most widely used interface language for relational database systems. Our approach aims to achieve better testing by selecting fault revealing databases. We propose the use of mutation analysis on SQL statements and discuss two scenarios for applying strong and weak mutation techniques. Experiments using real applications, real faults and real data were performed to: (i) evaluate the applicability of the approach, and (ii) compare fault-revealing abilities of input databases.

Planning and scheduling of testing activities play a key-role for any independent test team that performs tests for different software systems, pertaining to different development teams. Based on the work experience with several software systems within the scope of one real-world project, we propose an alternative approach that focuses on team efficiency to estimate the execution effort of a test case suite. To assess the validity of the approach data collected on test activity along one year were used. The approach is simpler to use than current methods, requiring less effort to apply; furthermore, it can provide the test team good estimates even using a small amount of data.

The relevance of reactive capabilities as a unifying paradigm for handling a number of database features and applications is well-established. Active database systems have been used to implement the persistent data requirements of applications on several knowledge domains. They extend passive ones by automatically performing predefined actions in response to events that they monitor. These reactive abilities are generally expressed with active rules defined within the database itself. We investigate the use of data flow-based testing to identify the presence of faults in active rules written in SQL. The goal is to improve reliability and overall quality in this realm. Our contribution is the definition of a family of adequacy criteria, which require the coverage of inter-rule persistent data flow associations, and its effectiveness in various data flow analysis precisions. Both theoretical and empirical investigations show that the criteria have strong fault detecting ability at a polynomial complexity cost.

Providing robust decision support for software engineering (SE) requires the collection of data across multiple contexts so that one can begin to elicit the context variables that can influence the results of applying a technology. However, the task of comparing contexts is complex due to the large number of variables involved. This works extends a previous one in which we proposed a practical and rigorous process for identifying evidence and context information from SE papers. The current work proposes a specific template to collect context information from SE papers and an interactive approach to compare context information about these studies. It uses visualization and clustering algorithms to help the exploration of similarities and differences among empirical studies. This paper presents this approach and a feasibility study in which the approach is applied to cluster a set of papers that were independently grouped by experts.

The number of scientific publications is constantly increasing, and the results published on Empirical Software Engineering are growing even faster. Some software engineering publishers have begun to collaborate with research groups to make available repositories of software engineering empirical data. However, these initiatives are limited due to data ownership and privacy issues. As a result, many researchers in the area have adopted systematic reviews as a mean to extract empirical evidence from published material. Systematic reviews are labor intensive and costly. In this paper, we argue that the use of Information Extraction Tools can support systematic reviews and significantly speed up the creation of repositories of SE empirical evidence.

Summary form only given. Databases and their applications are topics of interest to both academia and industry. However, they have received little attention towards improving the knowledge about their associated faults and failures. This lack of knowledge is an impediment to the definition of adequate software testing techniques applicable in this domain and to the development of quality software. We discuss issues arising from SQL manipulation failures and present the results of an investigation aiming at understanding the relationship between faults and failures. The analysis of data mapping indicates that: i) there is a many-to-many mapping between faults and failures; ii) failure dimensions are dependent on fault type, faulty command, and the database itself; and iii) knowledge of the manipulation faults is crucial to programming and testing database applications.

XML is a language frequently used for data representation and interchange in Web-based applications. In most cases, the XML documents must conform to a schema that defines the type of data that is accepted by a Web application. In this sense, an error in the schema or in the XML document can lead to failures in the application; the use of testing approaches, criteria and specific tools to ensure the reliability of data in the XML format is fundamental. We present a testing approach that helps to reveal faults in XML schemas. The test process involves generating XML documents with some modifications with respect to the original XML document and using queries to these documents to validate the schema. The XML documents and queries are generated according to a set of fault classes defined for the XML schemas. A case study applying the proposed approach is described and the results are presented.

Abstract Testing and debugging activities consume a significant amount of the software development and maintenance budget. To reduce this cost, the use of testing information for debugging purposes has been advocated. In general, heuristics are used to select structural testing requirements (nodes, branches and definition–use associations) more closely related to the manifestation of a failure, which are then mapped into a piece of code. The intuition is that the selected piece of code is likely to contain the fault. However, this approach has its drawbacks. Heuristics that select a manageable piece of code are less likely to hit the fault and the piece of code itself does not provide enough guidance for program understanding—a major factor in program debugging. These problems occur because this approach relies only on static information—a fragment of code. We introduce a strategy for fault localization that addresses these problems. The strategy—called the debugging strategy based on the requirements of testing (DRT)—is based on the investigation of indications (or hints) provided at run‐time by data‐flow testing requirements (definition–use associations). Our claim is that the selected definition–use associations may fail to hit the fault site, but still provide information useful for fault localization. The strategy's novelty and attractiveness are threefold: (i) the focus on dynamic information related to testing data; (ii) implementation in state‐of‐the‐practice symbolic debuggers with a low overhead; and (iii) the use of algorithms which consume constant memory and are linear on the number of branches in the program. A case study shows that our claim is valid (for the subject program) and a prototype tool implements the strategy. Copyright © 2004 John Wiley & Sons, Ltd.

A new technique and tool are presented for test data generation for path testing. They are based on the dynamic technique and on a Genetic Algorithm, which evolves a population of input data towards reaching and solving the predicates along the program paths. We improve the performance of test data generation by using past input data to compose the initial population for the search. An experiment was done to assess the performance of the techniques compared to that of random data generation.

A tool and techniques are presented for test data generation and identification of a path's likely unfeasibility in structural software testing. The tool is based on the dynamic technique and search using genetic algorithms. Our work introduces a new fitness function that combines control and data flow dynamic information to improve the process of search for test data. The unfeasibility issue is addressed by monitoring the genetic algorithm's search progress. An experiment shows the validity of the developed solutions and the benefit of using the tool.

Although there are many algorithms to draw hierarchical structures such as directed graphs and trees none specifically treat the problem of visualizing program graphs. This paper presents an algorithm and the underlying tool — ViewGraph — designed to visualize program graphs. The algorithm is divided in two parts: (1) determine node positions, and (2) assign routes to branches. The first part has three steps: level assignment, scope and position calculation. A modified algorithm used to assign levels to nodes in a tree is used to find Y coordinates; a concept called scope is used to define X coordinates. Scope is a prediction of the space required by a node and its descendants. A search on the available positions left by the placement of nodes is performed to route branches. A set of aesthetic aspects meant to help the development of program graph visualization algorithms is also proposed. The algorithm runs in an acceptable time making it useful even for interactive applications. © 1997 John Wiley & Sons, Ltd.

Real time systems is an application area where digital computer usage has grown significantly in the last decade. It has done so in parallel with the development of techniques and the proposition of software engineering approaches aiming at an effective and economical way of producing software. Real time applications present features distinct from other applications and impose suitable techniques and models for software development. One of the most well-known methodologies, "Structured Development for Real-time Systems", by Ward and Mellor (1985), was used as the model for prototype generation in a real-time prototyping system (ProTR); ProTR is described and its application is shown through an example.>

We are concerned here with the design of intelligent magnetic bubble memories which may be used to support high-level data base management functions. They may also be used to provide users with large work spaces in which elementary file processing operations may be performed without external intervention. It is our intent to explore ways of incorporating in the design of these memories the novel chip organizations and unique features of magnetic bubble memories. In particular, we evaluate the performance of various memory organizations and storage organizations for different magnetic bubble chips. Retrieval times per word and per page are the parameters used to evaluate the different memory organizations. Performance of hierarchical bubble memory systems are discussed. The data rearrangement operations capable of being carried out in bubble memories are incorporated in the design of elementary file processing operations and basic relational algebraic operations. An architecture of intelligent magnetic bubble memory designed to support the relational data model and to enhance a relational algebraic interface is described.

In this paper, we are concerned with the design of intelligent magnetic bubble memories. It is our intent to explore ways of incorporating the novel bubble chip organizations and bubble movement operations in the design of such memories. In particular, we evaluate the improvements in performance of various file processing algorithms and memory organizations which can be achieved through their use. Retrieval times per word and per page are the parameters used to evaluate the different memory organizations. Performance of hierarchical memory systems using bubble memories are discussed. The unique features of magnetic bubble memories are used in designing algorithms for elementary file processing operations such as sorting and merging and for basic relational algebraic operations.