Selection Of Software Components Research Articles

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Software Component Selection: An Optimized Selection Criterion for Component-based Software Engineering (CBSE)

Component-Based Software Engineering (CBSE) is gaining popularity in software development due to time and cost limitations. As software applications have become integral to people's lives, developing high-quality and user-friendly applications within a reasonable timeframe and budget has become increasingly challenging. Software development firms frequently use Commercial-Off-The-Shelf (COTS) components to address this challenge and reduce development costs and time. However, selecting appropriate components that meet customer requirements and integrate seamlessly with the target system is a complex task requiring considering the entire software system's quality. This study investigates the critical factors that software industry practitioners and experts must consider when selecting software components. First, the author asked practitioners to identify the most important quality criteria for an online bookstore from a list using subjective judgment and evaluation grades. Then, the study employed the Evidential Reasoning (ER) approach to tackle the multi-level evaluations and information uncertainty associated with software component selection issues. The ER approach's primary features, such as weight normalization, probability assessment, uncertainty management, and utility intervals, offer several benefits for COTS selection problems, including cost and time reduction, improved software reliability, effectiveness, and efficiency. This study assessed the quality criteria for an online bookstore using the ER approach and provided analysis results based on the approach's computational steps. Finally, the study ranked the four components according to their weights, evaluation grades, and belief degrees for selection

Enabling the Ocean Internet of Things with Renewable Marine Energy

Marine renewable energy can play an integral role in reducing the cost and complexity of collecting data from the oceans, and enhancing its exploitation. The processing and transmission of data at sea may be modelled as an `internet of things' (IoT) application. The `thing' is any offshore device which can collect and process data in-situ, while the `internet' represents the medium for transmitting data. IoT is differentiated from other internet based communication paradigms by the constraints on the system. These constraints are typically limited energy budgets and computing power, intermittent and low-bandwidth connectivity, and limited physical access. While land based IoT applications are already well served by a wide selection of hardware and software components, the needs of offshore IoT applications are not well served. Utilizing marine energy can advance the adoption of IoT at sea by providing in-situ energy generation, whilst also benefitting from the same technology. The Sustainable Energy Authority of Ireland funded `BlueBox' project aims to overcome barriers to entry for applying IoT technologies to offshore sensing by developing Ocean IoT (OIoT) hardware and software solutions. Features of the BlueBox system include modular offshore focussed hardware, no-code configuration and control of peripherals (such as sensors and actuators), duplex transmission of data using multiple media, serverless cloud server architecture and an edge computing framework. This paper presents an overview of BlueBox, tank tests for system validation of a prototype wave-energy powered ocean-observing platform, and a discussion of future applications of the technology.

Design and Implementation of Home Automation using Amazon Alexa

Abstract: The design and implementation of a home automation system using Amazon Alexa, a voice-controlled virtual assistant. With the rapid growth of smart home technology, integrating voice control capabilities has become a popular and convenient way to interact with home automation systems. The proposed system leverages the power of Amazon Alexa to enable users to control and monitor various household appliances through voice commands. The system consists of a cloud-based backend for data storage and processing, a smart home hub for device control, and a mobile application for user interaction. The hardware and software components, including devices, sensors, and communication protocols, are carefully selected and integrated into the system. The integration with Amazon Alexa Skills Kit allows the system to understand and process voice commands effectively. A case study is presented to demonstrate the practical application of the system in a typical home environment. The results showcase the system's efficiency, convenience, and user satisfaction. This project contributes to the growing field of home automation and highlights the potential of voice-controlled virtual assistants like Amazon Alexa in improving the functionality and usability of smart homes. The proposed system encompasses a cloud-based backend, a smart home hub, and a mobile application to facilitate seamless control and monitoring of various household appliances and devices through voice commands. The selection and integration of appropriate hardware and software components, including devices, sensors, and communication protocols, are thoroughly discussed. Furthermore, the system is integrated with the Amazon Alexa Skills Kit to enable effective processing of voice commands. A detailed case study is presented, highlighting the system's practical application within a typical home environment. The results demonstrate the system's efficacy, emphasizing its convenience, efficiency, and user satisfaction. This project contributes to the ever-growing field of home automation, showcasing the potential of voice-controlled virtual assistants, such as Amazon Alexa, to enhance the functionality and user experience of smart homes. Future enhancements and research directions are also discussed, paving the way for further advancements in this domain.

VALIDATION OPTIMIZATION ENVIRONMENT FOR IMPROVED SELECTION OF SOFTWARE COMPONENTS: CONCEPTUAL MODELING AND ARCHITECTURE

Responding to the needs of a user, the selection of software components for building computer applications is a crucial step. In the matter, optimization tools exist to experiment the developed models. However, these tools are not suitable for the user and especially for a non-expert. In this article, we propose to build a software environment validating our optimization model. It is therefore an automatic aid simulator for the choice of software components of websites for the construction of modular systems. By using the method of analysis in particular the unified process of UML, at the end of our conceptual modeling, the diagrams of classes, sequences and activities are obtained. Moreover, a simulator architecture, an operation algorithm and an optimization algorithm capable of improving the choice in the selection of software components among the existing software libraries are built. Our simulator system environment is nearing completion.

Software selection in large-scale software engineering: A model and criteria based on interactive rapid reviews

ContextSoftware selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns.ObjectiveOur main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment.MethodWe used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company.ResultsThe model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools.ConclusionsWe have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors.

Web Semantics and Ontologies based Framework for Software Component Selection from Online Repositories

Web Semantics and Ontologies based Framework for Software Component Selection from Online Repositories

Web semantics and ontologies-based framework for software component selection from online repositories

Web semantics and ontologies-based framework for software component selection from online repositories

A Synergy of Innovative Technologies towards Implementing an Autonomous DIY Electric Vehicle for Harvester-Assisting Purposes

The boom in the electronics industry has made a variety of credit card-sized computer systems and plenty of accompanying sensing and acting elements widely available, at continuously diminishing cost and size levels. The benefits of this situation for agriculture are not left unexploited and thus, more accurate, efficient and environmentally-friendly systems are making the scene. In this context, there is an increasing interest in affordable, small-scale agricultural robots. A key factor for success is the balanced selection of innovative hardware and software components, among the plethora being available. This work describes exactly the steps for designing, implementing and testing a small autonomous electric vehicle, able to follow the farmer during the harvesting activities and to carry the fruits/vegetables from the plant area to the truck location. Quite inexpensive GPS and IMU units, assisted by hardware-accelerated machine vision, speech recognition and networking techniques can assure the fluent operation of a prototype vehicle exhibiting elementary automatic control functionality. The whole approach also highlights the challenges for achieving a truly working solution and provides directions for future exploitation and improvements.

Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-Based Edge Device

Many application scenarios of edge visual inference, e.g., robotics or environmental monitoring, eventually require long periods of continuous operation. In such periods, the processor temperature plays a critical role to keep a prescribed frame rate. Particularly, the heavy computational load of convolutional neural networks (CNNs) may lead to thermal throttling and hence performance degradation in few seconds. In this paper, we report and analyze the long-term performance of 80 different cases resulting from running five CNN models on four software frameworks and two operating systems without and with active cooling. This comprehensive study was conducted on a low-cost edge platform, namely Raspberry Pi 4B (RPi4B), under stable indoor conditions. The results show that hysteresis-based active cooling prevented thermal throttling in all cases, thereby improving the throughput up to approximately 90% versus no cooling. Interestingly, the range of fan usage during active cooling varied from 33% to 65%. Given the impact of the fan on the power consumption of the system as a whole, these results stress the importance of a suitable selection of CNN model and software components. To assess the performance in outdoor applications, we integrated an external temperature sensor with the RPi4B and conducted a set of experiments with no active cooling in a wide interval of ambient temperature, ranging from 22 °C to 36 °C. Variations up to 27.7% were measured with respect to the maximum throughput achieved in that interval. This demonstrates that ambient temperature is a critical parameter in case active cooling cannot be applied.

The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm

The paper presents the swarm intelligence approach to the selection of a set of software components based on computational experiments simulating the desired operating conditions of the software system being developed. A mathematical model is constructed, aimed at the effective selection of components from the available alternative options using the artificial bee colony algorithm. The model and process of component selection are introduced and applied to the case of selecting Node.js components for the development of a digital platform. The aim of the development of the platform is to facilitate countrywide simultaneous online psychological surveys in schools in the conditions of unstable internet connection and the large variety of desktop and mobile client devices, running different operating systems and browsers. The module whose development is considered in the paper should provide functionality for the archiving and checksum verification of the survey forms and graphical data. With the swarm intelligence approach proposed in the paper, the effective set of components was identified through a directional search based on fuzzy assessment of the three experimental quality indicators. To simulate the desired operating conditions and to guarantee the reproducibility of the experiments, the virtual infrastructure was configured. The application of swarm intelligence led to reproducible results for component selection after 312 experiments instead of the 1080 experiments needed by the exhaustive search algorithm. The suggested approach can be widely used for the effective selection of software components for distributed systems operating in the given conditions at this stage of their development.

Automated Software Design Reusability using a Unique Machine Learning Technique

The era of machine learning (ML) has brought significant advancement into the traditional approaches of software development and services. Software reusability and design automation is a key requirement that can be handled through the integration of artificial intelligence (AI) capabilities with the traditional approach of software development lifecycle (SDLC) practices. The study introduces a novel approach of ML, which can assist inappropriate selection of reusable software components, which in the long run, can optimize the operational cost in the context of development practices and also speed up the service delivery performance of software engineering activities. The proposed model is validated through a numerical analysis that shows the effectiveness of the system in terms of both classification accuracy and computational efficiency.

A multi-period multi-objective optimization framework for software enhancement and component evaluation, selection and integration

Software can be delivered either after it has been entirely developed or it can be delivered in phases. When delivered in phases, software can be enhanced either by upgrading the existing modules in forthcoming phases or by modifying the modules that were developed in previous phases. An incorporated multi-period multiple objective optimization model for the enhancement and establishment of a software system is discussed in this paper. The software system’s total cost is minimized, and the fitness evaluation score of the software components (commercial off-the-shelf and in-house) for modules that are not outsourced, along with vendors for modules that are outsourced, is maximized. The fitness evaluation of module alternatives (software components and vendors) is conducted based on many important qualitative attributes using the Technique for Order Preference by Similarity to Ideal Solution. The model is constrained to critical parameters, such as compatibility, delivery time and integration of alternatives, along with software reliability. To exemplify the applicability of the suggested framework for optimization, the development of e-commerce software is used as a case study.

Engineering Software for a Mobile Robot Motion Control System

The paper centres round the problem of the engineering a motion control system for a mobile robot based on the effective selection of software components with respect to the numerical criterion proposed by the authors. The data for the selection process comes out the reproducible experiments with the sets of alternative components in a Gazebo virtual infrastructure simulating the real robot operating conditions. The genetic algorithm is used to reduce the number of experiments with unpromising sets of software components. The methodology proposed by the authors is applied to the real task of engineering a motion control system for a non-anthropomorphic mobile robot. The virtual infrastructure and genetic algorithm parameters are provided as well as the physical model of the robot for that task. To calculate the integral quality criterion proposed in the paper, 4 partial quality criteria were measured in the experiments with different software components. The motion process of the physical robot with the selected software components is shown.

Quantifying reusability of software components using hybrid fuzzy analytical hierarchy process (FAHP)-Metrics approach

The role of reusability cannot be neglected in the software component selection since it determines the worth of a component for its potential (re)use. The reusability is a qualitative feature and it is arduous to measure it directly. Typically, it is assessed subjectively without paying much heed to the involvement of befitting stakeholders and the use of a quantitative approach. We aim to quantify the reusability of a software component based on its quality through multi-criteria decision making (MCDM) solution to rank alternatives. To do this, the quality preferences of stakeholders about the reuse of components are determined. The reusability is quantified using a hybrid fuzzy analytic hierarchy process (FAHP) and quality metrics approach. In this approach, the weights of both FAHP and quality metrics are integrated to get the final ranking. FAHP-Metrics approach is applied to payment gateway (software) components, and the ranking of given components is obtained based on their reusability value. Finally, the comparative analysis of our results with respect to other recent studies reveals that the Spearman rank correlation result is highly significant and acceptable, and the obtained weights have a good association with other studies.

Recommendation-Based Component Selection for Component-Based Systems

Selection of reusable software components in software repositories to facilitate quality software development has always been a focal point and a big concern for software researchers. One of the most time-consuming tasks in software reusability is tracing and retrieving software components from a large repository. The selection of inapt software package can result in high cost and ultimately becomes a prime source of adverse outcome in business processes and performance of the organization. Creation of quality software depends upon the selection of the best set of components among all the alternatives present in the reusable component repository. Frequently used sets of components can be obtained by using mining algorithms. This paper proposes a component selection methodology and a method for retrieval of the optimal set of reusable components from the repository. Case-based retrieval is applied for initial filtering of components to narrow down the search space. A data mining algorithm is applied to extract the candidate set of components for a given case. Most frequent set of components aid the decision-maker to select the finest component set and also assist in suggesting the supplementary components for the case to match with the latest updates.

Proposal of Automatic Methods for the Reuse of Software Components in a Library

The increasing complexity of applications is constraining developers to use reusable components in component markets and mainly free software components. However, the selected components may partially satisfy the requirements of users. In this article, we propose an approach of optimization the selection of software components based on their quality. It consists of: (1) Selecting components that satisfy the customer's non-functional needs; (2) Calculate the quality score of each of these candidate components to select; (3) Select the best component meeting the customer's non-functional needs with linear programming by constraints. Our aim is to maximize this selection for considering financial cost of component and adaptation effort. Yet in the literature review, researchers are unanimous that software components reuse reduces the cost of development, maintenance time and also increases the quality of the software. However, the models already developed to evaluate the quality of the component do not simultaneously take into account financial cost and adaptation effort factors. So, in our research, we established a connection between the financial cost and the adaptation time of the selected component by a linear programming model with constraints. For our work's validation, we propose an algorithm to support the developed theory. User will then be able to choose the relevant software component for his system from the available components.

Software component identification and selection: A research review

SummaryNowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.

Software component evaluation and selection using TOPSIS and fuzzy interactive approach under multiple applications development

In this paper, a two phase approach is proposed for decision-making situation that involves optimal software component evaluation and selection for designing component-based modular software system with multiple applications. In the first phase, components are evaluated using technique for order preference by similarity to ideal solution. In the second phase, a non-linear multi-objective optimization model is developed that facilitates the decision whether “to buy commercial-off-the-shelf components” or “to build in-house components” so that the total score of alternative components is maximized while the overall cost and delivery time of the system are minimized. Many critical parameters such as reliability of various applications, reusability and compatibility of the software components are considered simultaneously in the proposed optimization model. To determine a preferred compromise solution for the multi-objective optimization problem, a fuzzy interactive approach is used. Numerical illustrations based on a small-scale case study are presented to demonstrate usefulness of the proposed optimization model for optimal “build or buy” decisions in real-world applications.

Clustering based Architecture for Software Component Selection

Clustering based Architecture for Software Component Selection