In an era of the industrial internet of things (IoT), data transferred or saved is always vulnerable to attacks. The IoT networks are needed for implementing security in IoT devices. The IoT networ...

Swarm intelligence in anomaly detection systems: an overview

Component based software development (CBSD) endeavors to deliver cost-effective and quality software systems through the selection and integration of commercially available software components. CBSD emphasizes the design and development of software systems using preexisting components. Software component reusability is an indispensable part of component based software development life cycle (CBSDLC), which consumes a significant amount of organization’s resources, that is, time and effort. It is convenient in component based software system (CBSS) to select the most suitable and appropriate software components that provide all the required functionalities. Selecting the most appropriate components is crucial for the success of the entire system. However, decisions regarding software component reusability are often made in an ad hoc manner, which ultimately results in schedule delay and lowers the entire quality system. In this paper, we have discussed the analytic network process (ANP) method for software component selection. The methodology is explained and assessed using a real life case study.

https://downloads.hindawi.com/journals/aaa/2014/535970.pdf

Software Component Selection Based on Quality Criteria Using the Analytic Network Process

This article has multiple objectives. First of all, the fundamental concepts and challenges of the research field known as Sentiment Analysis (SA) are presented. Secondly, a summary of a chronological account of the research performed in SA is provided as well as some bibliometric indicators that shed some light on the most frequently used techniques for addressing the central aspects of SA. The geographical locations of where the research took place are also given. In closing, it is argued that there is no hard evidence that fuzzy sets or hybrid approaches encompassing unsupervised learning, fuzzy sets and a solid psychological background of emotions could not be at least as effective as supervised learning techniques.

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Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis.

Complexity of Internet of Things IoT applications and difficulty to predict their behaviour in wireless communications make modelling of IoT units an important research topic. The IoT unit is considered here as the two-node IoT model that supports bi-directional wireless communications. There are many approaches to model security and energy awareness; however, little is known about the synergistic effect of those factors at the application level under the influence of environmental factors such as noise. The paper introduces a modelling framework to model the security-energy-environment issues as main attributes to allow defining quality of service QoS for the IoT-based applications. Among others, the healthcare ones are regarded as predominant now. We model IoT units using the feature-based modelling methodology adopted from the software engineering domain. The result of modelling is a set of feature models with valid configurations that describe the energy-security-environment-performance relationships and possible constraints to support various IoT applications. Having feature models, we can select a configuration that is best suited for a given IoT application with respect to QoS requirements. As feature models represent abstract relationships of domain factors, we need additionally to provide experiments to determine concrete values of modelled features. We describe the experimental system to measure energy consumption under the influencing factors. Both models abstract and concrete allow reasoning about QoS of the IoT applications. Copyright © 2016 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.1537

Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Monitoring and controlling energy distribution: Implementation of a distribution management system based on Common Information Model

Execution tracing and logging significantly influence the time spent on localizing software errors; consequently, they have essential impact on maintainability. Moreover, in certain situations these tools are the best suited instruments to analyse the behaviour of distributed, multithreaded or embedded applications. In spite of this, software product quality frameworks do not include execution tracing or logging as a quality property. In this paper we examine the extension possibilities of the present software product quality frameworks to accommodate execution tracing. In addition, the scope of the investigation includes facilities of the frameworks to address the uncertainty involved in quality measurements.

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Towards Introducing Execution Tracing to Software Product Quality Frameworks

Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000; moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper.

https://link.springer.com/content/pdf/10.1007/s42979-020-00140-z.pdf

Software Product Quality Models, Developments, Trends, and Evaluation

The quality of execution tracing impacts the time to a great extent to locate errors in software components; moreover, execution tracing is the most suitable tool, in the majority of the cases, for doing postmortem analysis of failures in the field. Nevertheless, software product quality models do not adequately consider execution tracing quality at present neither do they define the quality properties of this important entity in an acceptable manner. Defining these quality properties would be the first step towards creating a quality model for execution tracing. The current research fills this gap by identifying and defining the variables, i.e., the quality properties, on the basis of which the quality of execution tracing can be judged. The present study analyses the experiences of software professionals in focus groups at multinational companies, and also scrutinises the literature to elicit the mentioned quality properties. Moreover, the present study also contributes to knowledge with the combination of methods while computing the saturation point for determining the number of the necessary focus groups. Furthermore, to pay special attention to validity, in addition to the the indicators of qualitative research: credibility, transferability, dependability, and confirmability, the authors also considered content, construct, internal and external validity.

https://www.mdpi.com/2571-5577/4/1/20/pdf

Quality Properties of Execution Tracing, an Empirical Study

Execution tracing quality is a crucial characteristic which contributes to the overall software product quality though the present quality frameworks neglect this property. In the scope of this pilot study the authors introduce a process to create a model for describing execution tracing as a quality property; moreover, the performance of four different models created is compared. The process and the models presented are capable of capturing subjective uncertainty which is an intrinsic part of the quality measurement process. In addition, the possibility of linking the presented models to software product quality frameworks is also illustrated.

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Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of 23 previously identified distinct software product quality models are analysed on how they handle the abstract notion of quality, and a taxonomy on the quality manifestations that the individual software product quality models are able to capture is established. Quality models that are able to solely describe the quality manifestation of the source code are attractive due to their full automation potential through static code analysers, but their assessment results ignore a huge part of software product quality, which is the one that most impresses the end user. The manifestations of software product quality that address the behaviour of the software while it operates, or the perception of the end user with regard to the software in use, require human involvement in the quality assessment. The taxonomy contributes to interpreting the quality assessment results of different quality models by showing the possible quality manifestations that can be captured by the identified models; moreover, the taxonomy also provides assistance while selecting a quality model for a given project. The quality manifestations used for the quality measurement always need to be presented, otherwise the quality assessment results cannot be interpreted in an appropriate manner.

Tamas Galli

Papers

Towards Introducing Execution Tracing to Software Product Quality Frameworks

Software Product Quality Models, Developments, Trends, and Evaluation

Quality Properties of Execution Tracing, an Empirical Study

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

On the Use of Quality Models to Address Distinct Quality Views