Improvement of component selection process using Genetic Algorithm for Component-Based Software Development

Vijayalakshmi, K.; Ramaraj, N.; Amuthakkannan, R.

doi:10.1504/ijiscm.2008.019289

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. Vijayalakshmi, K., Ramaraj, N., and Amuthakkannan, R. ( 2008) proposed an automated approach based on a Genetic algorithm (GA) for component selection that deals with functional and non-functional requirements 26 . Neubauer, T., Pichler, J., and Stummer, C. ( 2008) evaluated two approaches with the Atana approach that allows decision-makers to make decisions semi-automatedly.…”

Section: Software Component Selection Methods/approachesmentioning

confidence: 99%

Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Nabot

2022

Preprint

View full text Add to dashboard Cite

Component-based software engineering (CBSE) is becoming the frequently used approach for software development concerning time and cost constraints. While most people’s lifestyle depends on the use of software applications, the development cost and time of software applications are becoming more difficult to achieve. Also, high-quality and competent applications that fit users’ needs became essential. Therefore, most software development organizations are reusing commercial-off-the-shelf (COTS) to reduce development costs and time. However, most organizations and developers face problems selecting components that fit customer needs to integrate with the target system. So, decisions regarding software component selection are hard to consider regarding the entire quality of the software system. This study aims to investigate the most important matters to software industry practitioners and experts involved in component selection. First, the author asked the practitioners to select the most important quality criteria for an online bookstore from a list by providing their subjective judgment and evaluation grades; after that, utilizing the evidential reasoning (ER) approach for software component selection problems due to its ability to analyze decisions with multilevel evaluations and information uncertainty. Moreover, the main features of the ER approach, such as weight normalization, probability assessment, dealing with uncertainty, and utility intervals, offer several advantages for COTS selection problems, including cost and time minimizing, improved software reliability, effectiveness, and efficiency. This study concentrated on assessing the quality criteria for an online bookstore using the ER approach. Analysis results are provided depending on the computational steps of this approach. Finally, the findings show the rank of the four components according to their weights, evaluation grades, and belief degrees for the selection.

show abstract

Section: Software Component Selection Methods/approachesmentioning

confidence: 99%

Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Nabot

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These approaches minimize system construction cost. Vijayalakshmi et al [2008] proposed a GA-based approach to select an optimized combination of components and Kuperberg et al [2008] proposed a GP-based platform-independent reengineered parametric behavior model for black-box components performance prediction.…”

Section: Design Tools and Techniquesmentioning

confidence: 99%

Search-based software engineering

2012

View full text Add to dashboard Cite

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE life cycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This paper 1 provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

show abstract

“…Baker describes ranking and selection as a feature set selection problem and shows that expert judgement in selecting sets of components from a database is outperformed by automated search algorithms [6]. Similarly, Vijayalakshmi et al apply a genetic algorithm with a WSM fitness function to select components from a large set of candidates [77]. DEER [18] focuses on the tasks of ranking and selection of components in the requirements stage of component-based development.…”

Section: Component Evaluation and Selectionmentioning

confidence: 99%

Improving component selection and monitoring with controlled experimentation and automated measurements

Becker

Rauber

2010

Information and Software Technology

View full text Add to dashboard Cite

a b s t r a c tContext: A number of approaches have been proposed for the general problem of software component evaluation and selection. Most approaches come from the field of Component-Based Software Development (CBSD), tackle the problem of Commercial-off-the-shelf component selection and use goal-oriented requirements modelling and multi-criteria decision making techniques. Evaluation of the suitability of components is carried out largely manually and partly relies on subjective judgement. However, in dynamic, distributed environments with high demands for transparent selection processes leading to trustworthy, auditable decisions, subjective judgements and vendor claims are not considered sufficient. Furthermore, continuous monitoring and re-evaluation of components after integration is sometimes needed. Objective: This paper describes how an evidence-based approach to component evaluation can improve repeatability and reproducibility of component selection under the following conditions: (1) Functional homogeneity of candidate components and (2) High number of components and selection problem instances. Method: Our evaluation and selection method and tool empirically evaluate candidate components in controlled experiments by applying automated measurements. By analysing the differences to system-development-oriented scenarios, the paper shows how the process of utility analysis can be tailored to fit the problem space, and describes a method geared towards automated evaluation in an empirical setting. We describe tool support and a framework for automated measurements. We further present a taxonomy of decision criteria for the described scenario and discuss the data collection means needed for each category of criteria. Results: To evaluate our approach, we discuss a series of case studies in the area of digital preservation. We analyse the criteria defined in these case studies, classify them according to the taxonomy, and discuss the quantitative coverage of automated measurements. Conclusion: The results of the analysis show that an automated measurement, evaluation and selection framework is necessary and feasible to ensure trusted and repeatable decisions.

show abstract

Improvement of component selection process using Genetic Algorithm for Component-Based Software Development

Cited by 6 publications

References 17 publications

Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Search-based software engineering

Improving component selection and monitoring with controlled experimentation and automated measurements

Contact Info

Product

Resources

About