Non-functional Properties Research Articles

Mutation Testing and Its Analysis on Web Applications for Defect Prevention and Performance Improvement

The society's increasing reliance on web applications with the growing online market and digitization of almost every service, there is an increasing demand for better reliability, security, and interoperability of web applications. Testing becomes an integral part of improving this reliability on web applications. Despite the innumerable number of tools, techniques, methods for testing web applications, there is still scope for expansion in the code coverage of web applications. Mutation testing with its expansive potential to expose vulnerabilities of web applications took a backseat owing to its exhaustive testing cycles. Some mutation operators related to security, performance, and other non-functional attributes of web applications are presented in the current work. In the current work, a thorough analysis of various mutations operators proposed by authors towards the non-existent operators thus far is presented. An augment of 47% of operators occurred in the present work. A concise discussion on the scope of work future direction of work is presented.

Non-functional requirements in the ELASTIC architecture

The new generation of smart systems require processing a vast amount of information from distributed data sources, while fulfilling non-functional properties related to real-time, energy-efficiency, communication quality and security. The ELASTIC software architecture is being developed to tackle this challenge, considering the complete continuum from the edge to the cloud. This paper provides a brief analysis of the smart application considered in the project, and the requirements emanating from their non-functional properties. The paper then identifies some of the technical constrains imposed to the ELASTIC software architecture to allow guaranteeing the non-functional requirements of the systems.

NDMF: Neighborhood-Integrated Deep Matrix Factorization for Service QoS Prediction

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called ${N}$ eighborhood-integrated ${D}$ eep ${M}$ atrix ${F}$ actorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

Why Developers Refactor Source Code

Refactoring aims at improving code non-functional attributes without modifying its external behavior. Previous studies investigated the motivations behind refactoring by surveying developers. With the aim of generalizing and complementing their findings, we present a large-scale study quantitatively and qualitatively investigating why developers perform refactoring in open source projects. First, we mine 287,813 refactoring operations performed in the history of 150 systems. Using this dataset, we investigate the interplay between refactoring operations and process (e.g., previous changes/fixes) and product (e.g., quality metrics) metrics. Then, we manually analyze 551 merged pull requests implementing refactoring operations and classify the motivations behind the implemented refactorings (e.g., removal of code duplication). Our results led to (i) quantitative evidence of the relationship existing between certain process/product metrics and refactoring operations and (ii) a detailed taxonomy, generalizing and complementing the ones existing in the literature, of motivations pushing developers to refactor source code.

Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to create therapy plans that improve their quality and quantity of life. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim of automatizing these parts of the CGA, we have designed and developed CLARC (smart CLinic Assistant Robot for CGA), a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Using CLARC to conduct geriatric tests will reduce the time medical professionals have to spend on purely mechanical tasks, giving them more time to develop individualised care plans for their patients. In fact, ideally, CLARC will perform these tests on its own. In parallel with the effort to correctly address the functional aspects, i.e., the development of the robot tasks, the design of CLARC must also deal with non-functional properties such as the degree of interaction or the performance. We argue that satisfying user preferences can be a good way to improve the acceptance of the robot by the patients. This paper describes the integration into the software architecture of the CLARC robot of the modules that allow these properties to be monitored at run-time, providing information on the quality of its service. Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot’s activity and its interaction with the patient covered by the non-functional properties that have been considered.

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

Visual Workflow Process Modeling and Simulation Approach Based on Non-Functional Properties of Resources

With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in which information systems, devices, and people must interact. It incorporates a number of fields including modeling and simulation (M&S). This paper focused on M&S of data workflow processes that are key steps to bridging the gap between business views and goals on the one side, and operational implementations on the other side. Simulation adds a dynamic view to static modeling; it increases understanding of the behavior of process mechanisms and the identification of weak areas in data flow. Several research projects have been focused on control flow and data flow, but less attention has been paid to resource characteristics. This work is based on the MDSEA approach and the eBPMN language, and proposes an approach that aims to distinguish the types of resources carrying out process tasks. Furthermore, it introduces a new composite resource made from the relationship between a user (human resource) and a task form (IT resource). Moreover, it proposes a resource aggregation based on process performance combination in order to run and display a global performance measurement of a process path.

Identifying How Nonfunctional Attributes Affect a System’s Lifecycle

Engineered systems often operate well beyond their planned retirement date due to a variety of factors from affordability to performance. This paper presents a grounded theory approach to identifying the nonfunctional attributes that separate Department of Defense (DoD) systems that the DoD extends beyond retirement and those that it chooses to retire. The systems engineering ilities, nonfunctional attributes, serve as a means to code textual data on these systems to develop and validate the grounded theory. It examines a total of fourteen DoD systems to first develop an initial grounded theory and then validate the grounded theory that the attributes of extensibility, flexibility, interoperability, robustness, and versatility are more common in extended DoD systems. The data indicates that these attributes differentiate the two groups of systems, retired and extended, at a statistically significant level and the decision to retire a DoD system is dependent on the nonfunctional attributes of these systems. By understanding what system attributes make a system more likely to be extended, engineering managers can better manage a system throughout its lifecycle. Finally, it presents potential future work to include the expansion of the grounded theory application to other systems and disruptive technologies.

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.

Fault tolerating multi-tenant service-based systems with dynamic quality

The rapid development of multi-tenant service-based systems (SBSs) is mainly due to the popularity of cloud computing. SBS combines the existing services in the form of business processes to meet the needs of multiple tenants. There are two kinds of services: functional and non-functional. Multiple services can have the same functionality while changing their non-functional properties, or QoS (Quality of Service). QoS value is an important criterion for service selection or recommendation. In the existing work, QoS value in service selection or recommendation is regarded as a constant. However, in the dynamic and volatile cloud environment, the performance of services may fluctuate due to various factors (e.g. techniques of service studied, network conditions, etc.). A constant value does not reflect the consistency of the service. This paper proposes FT4MTS-D (fault-tolerant strategies for multi-tenant service-based systems with dynamic quality), a novel method that evaluates the criticality of dynamic cost-effective fault tolerance strategies for multi-tenant service-based systems. First, the consistency of each given service is evaluated based on the feature vector of the cloud model. Second, this paper measures the consistency of given service by the sensitivity of probability degradation of an SBS in comparison to the probability degradation of that service. The goal is to find the critical services in the SBS and then determine the optimal fault-tolerant strategy for the given service. The experimental results show that FT4MTS-D has obvious advantages compared with several existing representative methods.

Automated fault tolerance augmentation in model-driven engineering for CPS

Cyber-Physical Systems are usually subject to dependability requirements such as safety and reliability constraints. Over the last 50 years, a body of efficient fault-tolerance mechanisms has been devised to handle faults occurring at run-time. However, properly implementing those mechanisms is a time-consuming task that requires a great deal of know-how. In this paper, we propose a general framework which allows system designers to decouple functional and non-functional concerns, and express non-functional properties at design time using domain-specific languages. In the spirit of generative programming, functional models are then automatically “augmented” with dependability mechanisms. Importantly, the real-time behavior of the initial models in terms of sampling times and meeting deadlines is preserved. The practicality of the approach is demonstrated with the automated implementation of one prominent software fault-tolerance pattern, namely N-Version Programming, in the CPAL model-driven engineering workflow.

Software Qualities and their Dependencies Report on two editions of the workshop

New trends in software engineering recently emerged to cope with even more complex systems which in turns highlight problems software shortfalls and defects. The SQUADE (Software QUAlities and their DEpendencies) workshop focuses on increasing the understanding of the nature of Software Qualities (SQs), i.e., nonfunctional properties or extra-functional requirements (e.g., reliability, security, maintainability, etc.), and their interrelationships with the aim of bringing them into practice of software engineering. The topic is highly relevant due to the current trend of designing and developing software-intensive systems with larger complexity, increased autonomy, higher speed of changes, and growing need for interoperability within systems of systems. Unfortunately, this new trend comes with more software shortfalls and defects, which are widely and publicly spread. The primary goal of the workshop is to bring together researchers and practitioners to build more solid foundations when dealing with software qualities.

Towards a Quality-of-Thing based approach for assigning things to federations

In the context of an Internet-of-Things (IoT) ecosystem, this paper discusses two necessary stages for managing federations of things. The first stage defines things in terms of duties and non-functional properties that define the quality of these duties. And, the second stage uses these properties to assign appropriate things to future federations. Specialized into ad hoc and planned, federations are expected to satisfy needs and requirements of real-life situations like traffic control that arise at run-time. A set of experiments using a mix of real and simulated datasets, demonstrate the technical doability of thing assignment to federations and are presented in the paper, as well.

Validating trustworthy service composition through VIPLE and pi-calculus

The current formal verification practice focuses on functionality and does not consider verification of the non-functional attributes. In this study, we propose a method in which non-functional attributes are incorporated into the logic rules of inference in terms of composition of the linear logic and pi-calculus. Giving the credibility to non-functional attributes is important, especially in the cloud computing platform and IoT environments, where trust and security are ultra-important. Such studies have not been paid much attention by researchers and practitioners. In our approach, the evolvement of the non-functional attributes is included in the process of formal verification of the service composition scheme. In addition to theoretical analysis, we applied a tool named Visual IoT/robotics Programming Language Environment (VIPLE) to execute and verify the validity of the service composition model's function. We translate the proving process of the linear logic into the corresponding pi-calculus expressions. VIPLE can translate visual work flow into pi-calculus and can verify the correctness of pi-calculus expressions.

A Semi-Automatic and Trustworthy Scheme for Continuous Cloud Service Certification

Traditional assurance solutions for software-based systems rely on static verification techniques and assume continuous availability of trusted third parties. With the advent of cloud computing, these solutions become ineffective since services/applications are flexible, dynamic, and change at runtime, at high rates. Although several assurance approaches have been defined, cloud requires a step-change moving current assurance techniques to fully embrace the cloud peculiarities. In this paper, we provide a rigorous and adaptive assurance technique based on certification, towards the definition of a transparent and trusted cloud ecosystem. It aims to increase the confidence of cloud customers that every piece of the cloud (from its infrastructure to hosted applications) behaves as expected and according to their requirements. We first present a test-based certification scheme proving non-functional properties of cloud-based services. The scheme is driven by non-functional requirements defined by the certification authority and by a model of the service under certification. We then define an automatic approach to verification of consistency between requirements and models, which is at the basis of the chain of trust supported by the certification scheme. We also present a continuous certificate life cycle management process including both certificate issuing and its adaptation to address contextual changes. Finally, we describe our certification framework and an experimental evaluation of its performance, quality, applicability, and practical usability in a real industrial scenario, which considers Engineering Ingegneria Informatica S.p.A. ENGpay online payment system.

An Introduction to Residuality Theory: Software Design Heuristics for Complex Systems

Abstract Residuality theory provides a basis for designing software systems with resilient and antifragile behaviour through understanding sensitivity to stress and the concept of residual behaviours. By considering systems as a set of residues that exist in connection to stressors, we can more easily understand the role of design decisions in the life cycle of software systems and the unpredictable complex contexts they exist in. Residuality theory provides an alternative to the vague methods by which OOP, SOA, and microservice approaches arrive at system designs and most importantly places non-functional properties as first class citizens of design efforts. Residuality theory allows us to consider business, software, and infrastructure architecture across many different platforms and paradigms and allows us to describe architecture in the same way regardless of perspective. It allows us to describe approaches both for functional and non-functional requirements and for design, delivery and operation of applications. Residuality Theory paves the way for expressing architectures as mathematical structures which makes approaches like Model Based Systems Engineering [1] possible.

Usage Analysis of Smartphone with Hierarchical Clustering

Functional and Non Functional attributes are the important factors of the Smartphone. This paper deals with how to group the characteristics of mobile phone using the clustering algorithms and then obtained the results after classify the related algorithms. Classify all the functional and non-functional attributes of the smartphone using the latest data mining algorithms using WEKA (Waikato Environment for Knowledge Analysis) open-source software to analyze data.

Genetic algorithm based estimation of non–functional properties for GPGPU programs

Abstract Non-functional properties, like execution time or memory access information, of programs running on graphics processing unit (GPUs) can raise safety and security concerns. For example, understanding the execution time is critical for embedded and real-time applications. To this end, worst-case execution time (WCET) is an important metric to check the real-time constraints imposed on embedded applications. For complex execution platforms, such as GPUs, analysis of WCET imposes great challenges due to the complex characteristics of GPU architecture as well as GPU program semantics. GPUs also have specific memory access behavior. Observing such memory access behavior may reveal sensitive information (e.g. a secret key). This, in turn, may be exploited to launch a side-channel attack on the underlying program. In this paper, we propose GDivAn , a measurement-based analysis framework for investigating the non-functional aspects of GPU programs, specifically, their execution time and side-channel leakage capacity. GDivAn is built upon a novel instantiation of genetic algorithm (GA). Moreover, GDivAn improves the effectiveness of GA using symbolic execution, when possible. Our evaluation with several open-source GPU kernels, including GPU kernels from the OpenSSL and MRTC benchmark suite, reveals the effectiveness of GDivAn both in terms of finding WCET and side-channel leakage.

Modeling, analyzing and predicting security cascading attacks in smart buildings systems-of-systems

Software systems intelligence and complexity have been continuously increasing to deliver more and more features to support business critical and mission critical processes in numerous domains such as defense, health-care, and smart cities. Contemporary software-based solutions are composed of several software systems, that form System-of-Systems (SoS). SoS differentiating characteristics, such as emergent behavior, introduce specific issues that render their security modeling, simulation and analysis a critical challenge. The aim of this work is to investigate how Software Engineering (SE) approaches can be leveraged to model and analyze secure SoS solutions for predicting high impact (cascading) attacks at the architecture stage. In order to achieve this objective, we propose a Model Driven Engineering method, Systems-of-Systems Security (SoSSec), that comprises: (1) a modeling language (SoSSecML) for secure SoS modeling and (2) Multi-Agent Systems (MAS) for security analysis of SoS architectures. To illustrate our proposed approach in terms of modeling, simulating, and discovering attacks, we have conducted a case study on a real-life smart building SoS, the Adelaide University Health and Medical School (AHMS). The results from this case study demonstrate that our proposed method discovers cascading attacks comprising of a number of individual attacks, such as a Denial of Service, that arise from a succession of exploited vulnerabilities through interactions among the constituent systems of SoS. In future work, we intend to extend SoSSec to address diverse unknown emergent behaviors and non-functional properties such as safety and trust.