scholarly journals Exponential Effort Estimation Model Using Unadjusted Function Points

2008 ◽  
Vol 7 (6) ◽  
pp. 830-839 ◽  
Author(s):  
T.W. Koh ◽  
M.H. Selamat ◽  
A.A.A. Ghani
Keyword(s):  
Effort Estimation ◽  
Estimation Model ◽  
Function Points

The software maintenance project effort estimation model based on function points

2003 ◽  
Vol 15 (2) ◽  
pp. 71-85 ◽  
Author(s):  
Yunsik Ahn ◽  
Jungseok Suh ◽  
Seungryeol Kim ◽  
Hyunsoo Kim
Keyword(s):  
Software Maintenance ◽  
Effort Estimation ◽  
Estimation Model ◽  
Function Points ◽  
Model Based ◽  
Project Effort

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

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 MINING EFFORT DATA FROM THE OSS REPOSITORY OF DEVELOPER’S BUG FIX ACTIVITY

2016 ◽  
Vol 3 (1) ◽  
pp. 107-128
Author(s):  
Syed Nadeem Ahsan ◽  
Muhammad Tanvir Afzal ◽  
Safdar Zaman ◽  
Christian Gütel ◽  
Franz Wotawa
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
State Of The Art ◽  
Tracking System ◽  
Previous History ◽  
Effort Estimation ◽  
Estimation Model ◽  
Activity Data ◽  
Actual Time ◽  
History Of

During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort. Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs.

scholarly journals EFFORT ESTIMATION MODELING OF E-GOVERNMENT APPLICATION DEVELOPMENT USING FUNCTION POINTS BASED ON TOR AND SRS DOCUMENT

2020 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Noorlela Marcheta
Keyword(s):  
Cost Estimation ◽  
Functional System ◽  
Effort Estimation ◽  
Software Cost Estimation ◽  
Application Development ◽  
Cost Estimates ◽  
Software Cost ◽  
Information Systems Management ◽  
Function Points ◽  
Requirements Specifications

Software cost estimation is important for information systems management and is generally taught in software engineering courses especially to terms of the ever-increasing development of E-Government. A significant challenge for Software Sizing (SS) is to determine cost estimates based on TOR documents that do not yet contain complete Software Requirements Specifications (SRS). This study uses Function Points as one of the measurement methods that can make cost estimates and expert needs based on the desired functional system. On the other hand, there are cases where estimation needs after the SRS document have been made. Thus in this study, the authors discuss the implementation of SS based on TOR and SRS documents for E-Government. The results of this study indicate the closeness of the actual and estimated values of 81.9% for TOR and 93.4% for SRS.

Duplex output software effort estimation model with self-guided interpretation

2018 ◽  
Vol 94 ◽  
pp. 1-13 ◽  
Author(s):  
Solomon Mensah ◽  
Jacky Keung ◽  
Michael Franklin Bosu ◽  
Kwabena Ebo Bennin
Keyword(s):  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model
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