An Overview of Web Effort Estimation

Mendes, Emilia

doi:10.1016/s0065-2458(10)78005-0

Cited by 5 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[23], [27], [29] DTs are not used only for classification but also for regression issues. [12], [22], [27], [29], [30], [37], [38], [46], [47] Limitations Supporting studies It cannot extrapolate beyond the data on which it was trained.…”

Section: Advantagesmentioning

confidence: 99%

See 1 more Smart Citation

Systematic Review Study of Decision Trees based Software Development Effort Estimation

Najm¹,

Zakrani²,

Marzak³

2020

IJACSA

View full text Add to dashboard Cite

The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.

show abstract

Section: Advantagesmentioning

confidence: 99%

“…[11], [12], [17], [18], [28], [30], [35], [40], [41], [46], [48], [49] Not perform better on large datasets.…”

Section: Advantagesmentioning

confidence: 99%

Systematic Review Study of Decision Trees based Software Development Effort Estimation

Najm¹,

Zakrani²,

Marzak³

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Web Cost Estimation (WCE) is the procedure for predicting the expected amount of effort that is required for web development. It involves making cost projections prior to web development in order to ensure efficient allocation of resources to enable timely project delivery, according to the financial plan [1]. WCE can be achieved by using expert judgment, evaluating parametric equations, or application of machine learning (ML) techniques.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Web Cost Estimation is the act of predicting the amount of effort required in order to execute a web development project. Specifically, it entails determining the costs of development, and the amount of resources needed to ensure efficient and timely project delivery, that are within budget [1]. Effective web cost estimate is crucial for the success of web project management, because it assists project managers to manage costs, plan for potential risks, improve development practices, and help to ensure prompt completion of projects within budget [2].…”

Section: Introductionmentioning

confidence: 99%

“…Effective web cost estimate is crucial for the success of web project management, because it assists project managers to manage costs, plan for potential risks, improve development practices, and help to ensure prompt completion of projects within budget [2]. Machine learning (ML) techniques have proved to be more precise for software cost estimation, and by extension web cost estimation when compared to other techniques such as expert judgments, or algorithmic models [1]. Several researchers have conducted comparative studies on the use of ML techniques for web cost estimation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Experimental Comparison of Three Machine Learning Techniques for Web Cost Estimation

Daramola¹,

Ajala²,

Akinyemi³

2016

IJSEIA

View full text Add to dashboard Cite

Many comparative studies on the performance of machine learning (ML) techniques for web cost estimation (WCE) have been reported in the literature. However, not much attention have been given to understanding the conceptual differences and similarities that exist in the application of these ML techniques for WCE, which could provide credible guide for upcoming practitioners and researchers in predicting the cost of new web projects. This paper presents a comparative analysis of three prominent machine learning techniques -Case-Based Reasoning (CBR), Support Vector Regression (SVR) and Artificial Neural Network (ANN)in terms of performance, applicability, and their conceptual differences and similarities for WCE by using data obtained from a public dataset (www.tukutuku.com). Results from experiments show that SVR and ANN provides more accurate predictions of effort, although SVR require fewer parameters to generate good predictions than ANN. CBR was not as accurate, but its good explanation attribute gives it a higher descriptive value. The study also outlined specific characteristics of the 3 ML techniques that could foster or inhibit their adoption for WCE.Handling of Non-linear relationship / coarse data: SVR and ANN have more in common in terms of their ability to handle non-linear and coarse data and have significant strength in this area compared to CBR Selection of training parameter: while there is a structural approach for selecting the training parameters for CBR, such activities with regards to SVR and ANN are experimental and based on trial and error. However, SVR require less amount of parameter selection during training compared to ANN Approach to solving Regression: Both SVR and ANN used an optimization approach, while CBR uses nearest neighbourhood derived by computing the degree of similarity Mode of Generalization: While CBR generalizes locally, both SVR and ANN are global.Explanation Capability: CBR have good explanation mechanism, which facilitate understanding of relationship among attributes and eventual results. This enables more user-involvement in performing the estimation task. In contrast, SVR and ANN aside from predicted result that is returned, do not provide any other details pertaining to attribute relationships, influence of attribute on the effort, and relevance of attribute, which can aid user's understanding. Effectiveness with Sparse Data:Not having large amount of data is not a disadvantage for both SVR and ANN, but this is a weakness for CBR. Nature of Technique:Generally, the use of CBR is more transparent and it is easier for the user to understand the inner workings of the system in terms of nature of computation and strength of relationship among attributes. SVR and ANN do not reveal the internal workings of the system but only returns relatively accurate estimates. It will be more difficult to ascertain whether the model has been built correctly, and validate whether the correct model has been built.

show abstract

Technological aspects of public tourism communication in Italy

Baggio

Mottironi

Corigliano

2011

Journal of Hospitality and Tourism Technology

View full text Add to dashboard Cite

Purpose: This paper aims at evaluating the online presence of Italian destination management organizations (DMOs) by assessing the quality of their websites through a demand-side heuristic approach. Moreover, given the importance of a clear strategic vision for an efficient and effective use of online media, the paper examines the alignment between tourism development plans and practical implementation for what regards ICTs. Finally, the possible impact of the adoption of a strategic approach on the overall quality of the websites under investigation is explored.Design/methodology/approach: A double analysis was conducted. An assessment of the main quality features of the official tourism websites and an evaluation of the statements of Public Authorities concerning strategic intentions and guidelines for developing ICT instruments. The two evaluations are compared in order to examine their relationships. A strategic matrix of the technological performance and attitude of Italian regional authorities is proposed.Findings: The quality of the Italian DMOs' websites is satisfactory, on average. Weaknesses are found in the offer of interactive services and Web 2.0 functions. Moreover, an improvement in the user evaluations with respect to the past demonstrates the attention given to ICTs by the DMOs. As a matter of fact, the strategic plans examined deal, in general, relatively well with ICT issues, while it is also shown that poor strategic vision, as expressed in the promotional plans, is related to poor performance of some websites.Originality/value: The paper assesses the quality of Italian DMOs' websites quality as perceived by visitors and, for the first time, relates the evaluations to how ICT strategic plans deal with the issue.

show abstract

An Overview of Web Effort Estimation

Cited by 5 publications

References 46 publications

Systematic Review Study of Decision Trees based Software Development Effort Estimation

Systematic Review Study of Decision Trees based Software Development Effort Estimation

An Experimental Comparison of Three Machine Learning Techniques for Web Cost Estimation

Technological aspects of public tourism communication in Italy

Contact Info

Product

Resources

About