376 research outputs found
A New Optimized Hybrid Model Based On COCOMO to Increase the Accuracy of Software Cost Estimation
The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order to increase the accuracy of this process, estimation models have to be revised. The Constructive Cost Model (COCOMO) has often been referred as an efficient model for software cost estimation. The popularity of COCOMO is due to its flexibility; it can be used in different environments and it covers a variety of factors. In this paper, we aim to improve the accuracy of cost estimation process by enhancing COCOMO model. To this end, we analyze the cost drivers using meta-heuristic algorithms. In this method, the improvement of COCOMO is distinctly done by effective selection of coefficients and reconstruction of COCOMO. Three meta-heuristic optimization algorithms are applied synthetically to enhance the process of COCOMO model. Eventually, results of the proposed method are compared to COCOMO itself and other existing models. This comparison explicitly reveals the superiority of the proposed method
Comparative Analyisis of Software Cost Estimation Project using Algorithmic Method
Software Cost Estimation has become an important factor to determine the efficiency of software development. There are many model of cost estimation like algorithmic model, top-down, and expert judgement. From all those models, Development in Algorithmic model is higher than the others. In this paper we present a comparative analysis of software cost project using algorithmic methods
Insights on Research Techniques towards Cost Estimation in Software Design
Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript
Software Project Duration Estimation Using Metrix Model
This paper analyzes the existing types of duration estimation models for software projects and advances a new, user-friendly model for project duration estimation. The Metrix model is a stochastic model for software project duration estimation using Monte Carlo simulation over an activity graph. The first advantage of using the Metrix model is that it produces a probability distribution of the software project duration and not a single estimate for duration. Further to this approach, we diminish the project uncertainty by giving the manager better control over the project duration and the associated probability of a certain duration outcome. The second advantage of the Metrix model is that for Monte Carlo simulation it relies upon the historic duration estimation of the team members and not on probability distribution functions which are rather difficult to come with. JEL classification: L86 computer software, O22 project analysis.Software project duration estimation, Monte Carlo simulation, Metrix model.
Approaches to Calculating Systems Engineering Schedule in Parametric Cost Models
This paper provides two similar approaches for calculating project schedule from a systems engineering perspective. To
illustrate the genesis of each approach, we provide two analogies; one from economics and one from physics. These are complemented with mathematical derivations that provide quantitative comparisons of the tradeoff between the duration of a project and the optimal effort. Connections are made to Books’ Law and Parkinson’s Law to validate the theoretical discussion
with the pragmatic observations from the software engineering literature
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
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