Analogy‐based effort estimation: a new method to discover set of analogies from dataset characteristics

Azzeh, Mohammad; Nassif, Ali Bou

doi:10.1049/iet-sen.2013.0165

Cited by 37 publications

(40 citation statements)

References 29 publications

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“…Finally, the smallest cluster where the 1NN of the new case is located become its analogues, the k value is the size of this cluster. The termination criterion [1] is based on cluster compactness (see Eq.3). Compactness is a measure that indicate the degree of similarity within clusters.…”

Section: Determining the Number Of Analoguesmentioning

confidence: 99%

“…In this study, we selected 8 common methods, of which 5 are fixed-k and the other 3 are dynamic-k. The results from many empirical studies [1,2,9,10] endorsed the effectiveness of dynamic-k over fixed-k methods, but some of which produced conflicting results [10]. This therefore introduces the unstable conclusion, an issue widely recognized in software effort estimation where different studies in the same area provide greatly diversified conclusions [6,13].…”

Section: Introductionmentioning

confidence: 96%

“…According to our survey, there are many approaches to determine the k value for a local dataset, [1,2,9,10,7,11,12,17]. Despite being greatly diversified, these approaches are commonly categorized into two groups: (1) statically assign a single k value to the entire local dataset commonly referred to as fixed-k, and (2) dynamically learn the k value that fits with the given training/test instance commonly referred to as dynamic-k.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we focus on a commonly used estimation method named analogy-based effort estimation (ABE) [1,9,10], a widely accepted method in terms of robustness, accuracy, and reliability [2,7,11,12,17]. Shepperd and Schofield [16] introduced the principle of ABE: Projects that are similar with respect to a set of project features will also be similar with respect to the effort.…”

Section: Introductionmentioning

confidence: 99%

“…Our performance measures consist only robust error measures suggested by Foss et al [4] (M AR, M dAR, SD, RSD and LSD), and these measure are ensured its statistical significance using the Wilcoxon rank-sum test [3]. The Wilcoxon test is commonly used in many studies such as in [1,9,10]. Our replicated experiments aim at finding a more desirable method to determine the k value for different situations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A review and comparison of methods for determining the best analogies in analogy-based software effort estimation

Chinthanet

Phannachitta

Kamei

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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Analogy-based effort estimation (ABE) is a commonly used software development effort estimation method. The processes of ABE are based on a reuse of effort values from similar past projects, where the appropriate numbers of past projects (k values) to be reused is one of the long-standing debates in ABE research studies. To date, many approaches to find this k value have been continually proposed. One important reason for this inconclusive debate is that different studies appear to produce different conclusions of the k value to be appropriate. Therefore, in this study, we revisit 8 common approaches to the k value being most appropriate in general situations. With a more robust and comprehensive evaluation methodology using 5 robust error measures subject to the Wilcoxon rank-sum statistical test, we found that conflicting results in the previous studies were not mainly due to the use of different methodologies nor different datasets, but the performance of the different approaches are actually varied widely.

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Section: Determining the Number Of Analoguesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A review and comparison of methods for determining the best analogies in analogy-based software effort estimation

Chinthanet

Phannachitta

Kamei

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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A differential evolution-based model to estimate the software services development effort

Bardsiri

Hashemi

2015

J. Softw. Evol. and Proc.

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Accurate estimation of software service development effort is a great challenge both in industry and for academia. The concept of effort is an important and effective parameter in process development and software service management. The reliable estimation of effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular effort estimation methods is analogy‐based estimation (ABE) to compare a service with similar historical cases. Unfortunately, ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and differential evolution algorithm to estimate the software services development effort. In fact, the differential evolution algorithm was utilized for weighing features in the similarity function of the ABE method. This weighing process could help determining the importance level of the various service features and extracting the best similar historical case. The proposed hybrid model has been evaluated on two real datasets and two artificial datasets. The obtained results were compared with common effort estimation methods. This comparison showed more accuracy, faster convergence, and lower cost of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.

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Analyzing the relationship between project productivity and environment factors in the use case points method

Azzeh

Nassif²

2017

J Softw Evol Proc

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Project productivity is a key factor for producing effort estimates from use case points (UCP), especially when the historical dataset is absent. The first versions of UCP effort estimation models used a fixed number or very limited numbers of productivity ratios for all new projects.These approaches have not been well examined over a large number of projects, so the validity of these studies was a matter for criticism. The newly available large software datasets allow us to perform further research on the usefulness of productivity for effort estimation of software development. Specifically, we studied the relationship between project productivity and UCP environmental factors, as they have a significant impact on the amount of productivity needed for a software project. Therefore, we designed 4 studies, using various classification and regression methods, to examine the usefulness of that relationship and its impact on UCP effort estimation. The results we obtained are encouraging and show potential improvement in effort estimation. Furthermore, the efficiency of that relationship is better over a dataset that comes from industry because of the quality of data collection. Our comment on the findings is that it is better to exclude environmental factors from calculating UCP and make them available only for computing productivity. The study also encourages project managers to understand how to better assess the environmental factors, as they do have a significant impact on productivity. KEYWORDS environmental factors, software effort estimation, software productivity, use case points 1 | INTRODUCTION Software effort estimation is necessarily required at the inception phase to bid for a software project and assign human resources. 1-45 Since very little data are known at this stage, managers turn their attention to software size metrics, which can help in providing a general sense about the most likely effort of a software project. Functional size metrics, such as function points (FP) 5 and use case points (UCP), 6 are well suited measures for such a problem. The FP has long been studied and examined as a more efficient size measure. 7,8 Nevertheless, measuring FP is tedious and requires intensive care, especially counting FP components such as internal, external, and query functions, in addition to evaluating environmental and complexity factors. 3,9 More recently, the UCP method has attracted more attention from researchers as a competitive sizing method to FP at the early phases of software development. The UCP was first introduced by Karner 6 in 1993 to estimate the size of object-oriented software projects. The basic idea of constructing UCP was inspired by the FP method. The typical input of UCP is the UML use case diagram, which describes the functional requirements in UML annotations such as actors, use cases, and various kinds of relations. 10 The UCP is computed by converting the elements of use case diagrams into their corresponding size metrics through a well-defined procedure. The accuracy of the UCP ...

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Analogy‐based effort estimation: a new method to discover set of analogies from dataset characteristics

Cited by 37 publications

References 29 publications

A review and comparison of methods for determining the best analogies in analogy-based software effort estimation

A review and comparison of methods for determining the best analogies in analogy-based software effort estimation

A differential evolution-based model to estimate the software services development effort

Analyzing the relationship between project productivity and environment factors in the use case points method

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