Integrating Function Point Project Information for Improving the Accuracy of Effort Estimation

2008 ◽  
Author(s):  
Faheem Ahmed ◽  
Salah Bouktif ◽  
Adel Serhani ◽  
Issa Khalil
Keyword(s):  
Effort Estimation ◽  
Function Point

scholarly journals Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort

2018 ◽  
Vol 3 (2) ◽  
pp. 90-112 ◽  
Author(s):  
Masanari Kondo ◽  
Osamu Mizuno ◽  
Eun-Hye Choi
Keyword(s):  
Causal Effect ◽  
Task Assignment ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Effect Analysis ◽  
Explanatory Variables ◽  
Function Point ◽  
New Findings ◽  
Non Gaussian

Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high quality software. Function Point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidences for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.

scholarly journals Revised approach for the prediction of functional size of mobile application

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziema Mushtaq ◽  
Abdul Wahid
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Unified Modeling Language ◽  
Effort Estimation ◽  
Sequence Diagrams ◽  
Unified Modeling ◽  
Development Cycle ◽  
Point Analysis ◽  
Function Point ◽  
Functional Size

Mobile applications affect our everyday activities and have become more and more information centric. Effort estimation for mobile application is an essential factor to consider in the development cycle. Due to feature complexities and size, effort estimation of mobile applications poses a continued challenge for developers. This paper attempts to adapt COSMIC Function Point and Unified Modeling Language (UML) techniques to estimate the size of a given mobile application. The COSMIC concepts capture data movements of the functional processes whereas the UML class analyzes them. We utilize the Use Case Diagrams, sequence diagrams and class diagrams for mapping the Function user requirements for sizing mobile applications. We further present a new size measurement technique; Unadjusted Mobile COSMIC Function points (UMCFP) to get the functional size of mobile application using Mobile Complex Factors as an input. In this study eight mobile applications were analyzed using UMCFP, Function Point Analysis and COSMIC Function Point. The results were compared with the actual size of previous Mobile application projects.

scholarly journals An Accurate FFPA-PSR Estimator Algorithm and Tool for Software Effort Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Senthil Kumar Murugesan ◽  
Chidhambara Rajan Balasubramanian
Keyword(s):  
Performance Metrics ◽  
Development Stage ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Reliability Factor ◽  
Function Point Analysis ◽  
Software Companies ◽  
Point Analysis ◽  
Function Point ◽  
Project Data

Software companies are now keen to provide secure software with respect to accuracy and reliability of their products especially related to the software effort estimation. Therefore, there is a need to develop a hybrid tool which provides all the necessary features. This paper attempts to propose a hybrid estimator algorithm and model which incorporates quality metrics, reliability factor, and the security factor with a fuzzy-based function point analysis. Initially, this method utilizes a fuzzy-based estimate to control the uncertainty in the software size with the help of a triangular fuzzy set at the early development stage. Secondly, the function point analysis is extended by the security and reliability factors in the calculation. Finally, the performance metrics are added with the effort estimation for accuracy. The experimentation is done with different project data sets on the hybrid tool, and the results are compared with the existing models. It shows that the proposed method not only improves the accuracy but also increases the reliability, as well as the security, of the product.

Application of Function Point Estimating

2014 ◽  
Vol 644-650 ◽  
pp. 3357-3360
Author(s):  
Chang Hong Zhou
Keyword(s):  
Data Gathering ◽  
Estimation Method ◽  
Effort Estimation ◽  
Integrated Monitoring ◽  
Point Counts ◽  
Point Analysis ◽  
Function Point ◽  
Monitoring Module ◽  
External Interface ◽  
High Level

Function point estimating is an important method for system of effort estimation. This article is based on a telecommunication surveillance module system-integrated monitoring module as example. It explains how this estimation method is applied in the project measurement process in detail.Function Point AnalysisFunction point analysis evaluates the functionality of a software system from the software end users perspective. Software functionality comes down to five basic functional elements[1], two of which represent end user demand for data: internal logical files (ILF) and external interface files (EIF). The other three are data gathering and processing features: external inputs (EI), external outputs (EO), external inquiries (EQ)[2].To determine the complexity of each functional element, the following data items are defined: record element type (RET), file type referenced, (FTR), data e1ement type (DET)[3]. To determine all functionalities complexity level, each data and transactional capabilities is assigned with low, average and high level based on standard matrix, see table 1 complexity matrix[4]. After determining the complexity of each feature, using the complexity value defined in table 2[5] multiply by the corresponding function point counts, accumulate to get the totals.Table 1: complexity matrixTable 2: IFPUG unadjusted function point basis

scholarly journals Web Effort Estimation: Function Point Analysis vs. COSMIC

2016 ◽  
Vol 72 ◽  
pp. 90-109 ◽  
Author(s):  
Sergio Di Martino ◽  
Filomena Ferrucci ◽  
Carmine Gravino ◽  
Federica Sarro
Keyword(s):  
Effort Estimation ◽  
Function Point Analysis ◽  
Point Analysis ◽  
Function Point ◽  
Estimation Function
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