Component Comparison, Evaluation, and Selection: A Continuous Approach

Ernst, Neil A.; Kazman, Rick; Bianco, Philip

doi:10.1109/icsa-c.2019.00023

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Some works have focused on the evaluation of a set of pre-established candidate technologies and do not deal with the problem of searching/retrieving the technologies from (Web) repositories. For example, given a set of predefined candidates, Ernst et al 38 proposes a scorecard to help developers to select a given technology. The scorecard is based on performance, maintenance, and community criteria.…”

Section: Related Workmentioning

confidence: 99%

Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories

Vázquez¹,

Pace²,

Marcos³

et al. 2022

Preprint

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The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growing number of technology options available. A common strategy is to browse software repositories via search engines (e.g., NPM, or Google), although it brings some problems. First, given a technology need, the engines might return a long list of results, which often causes information overload issues.Second, the results should be ranked according to criteria of interest for the developer.However, deciding how to weight these criteria to make a decision is not straightforward.In this work, we propose a two-phase approach for assisting developers to retrieve and rank JS technologies in a semi-automated fashion. The first-phase (ST-Retrieval) uses a meta-search technique for collecting JS technologies that meet the developer's needs. The second-phase (called ST-Rank), relies on a machine learning technique to infer, based on criteria used by other projects in the Web, a ranking of the output of ST-Retrieval. We evaluated our approach with NPM and obtained satisfactory results in terms of the accuracy of the technologies retrieved and the order in which they were ranked.

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Section: Related Workmentioning

confidence: 99%

Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories

Vázquez¹,

Pace²,

Marcos³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Ernst et al [22] describe emerging research related to component selection using high-level quality attribute indicators, project health measures, and a context-specific aggregation function for producing a single yes/no decision for integrators. The authors' proposal involves several aspects to evaluate components according to the context.…”

Section: A Software Components Selection and Evaluationmentioning

confidence: 99%

Involving Stakeholders in the Implementation of Microservice-Based Systems: A Case Study in an Ambient-Assisted Living System

et al. 2021

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“…Finally, the authors developed a reservation system on the basis of different requirements to effectively evaluate the proposed model. In [48] a scorecard-based CS method is proposed based on high-level quality attribute indicators, project health measures, and a context-specific aggregation function to provide an explicit decision (yes or no) for integrators. Kaur et al [49] developed a software CS architecture on the basis of the clustering concept.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

2021

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Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

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Component Comparison, Evaluation, and Selection: A Continuous Approach

Cited by 8 publications

References 16 publications

Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories

Retrieving and Ranking Relevant JavaScript Technologies from Web Repositories

Involving Stakeholders in the Implementation of Microservice-Based Systems: A Case Study in an Ambient-Assisted Living System

A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

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