Software Effort Estimation Based on Use Case Reuse (Back Propagation)

Abstract

Project managers often use effort estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting accuracy in Effort Estimation has always been difficult. In this study, the estimation of software development effort was determined using a back propagation model. This model\\\'s goal is to investigate the capabilities and potential uses of Utilizing artificial neural networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results.

Introduction

I. INTRODUCTION

The action of estimating a time necessary to create software is called effort estimation. Estimating the work is a critical job in the software industry. To produce accurate estimates, many computational models have been developed. Initial estimates without a clear understanding of the needs are inaccurate, but as the project advances, estimate accuracy increases. Choosing the right estimating technique is crucial as a result. Estimates of the effort can be utilized as input in budgets, investment plans, iteration plans, and project plans evaluations, pricing methods, and bidding rounds. Since at least the 1960s, The problem of software development effort estimation has been addressed by researchers and practitioners in the field of software. project. The biggest difficulty in project scheduling in the software industry is deciding how much of the project's resources should go toward the testing phase. It has been discovered that the testing phase often uses between 40% and 50% of the resources.

Estimating the specific amount of work that has to be put into the testing phase is quite difficult, though. As a result, the project planning is flawed. Inadequate testing of a project could cause the company to suffer severe losses. The study's primary focus has been on creating formal models for estimating software effort. Software effort estimation has been investigated using a range of methodologies, supporting vector regression (SVR) [4], radial basis function (RBF) neural networks [1], bagging predictors [9], and more modern machine learning techniques, such as the COCOMO [12] and COCOMO [12]. Machine learning techniques create models using data from previous work., which are then used to predict how much work will go into upcoming ventures. The vast majority of techniques for calculating software effort only offer estimates [1][4][7][9][10]. However, in addition to the estimate, it would be important to include estimates accuracy measures [10]. As a result, an estimation technique would be able to give a range of accuracy for where the effort would fall.

II. LITERATURE SURVEY

A review of several recent studies on software project effort estimation is provided in this section. Numerous publications have lately been published in this area of current study. We concentrate our review in this section on a few significant article that used in machine learning to calculate software projects effort. Despite extensive research over the past 20 years, the software industry still faces significant challenges when it comes to accurate resource estimation. Authors have occasionally discussed different approaches to the problem. Jorgensen provided a detailed examination of numerous studies on the development effort. 10. Wavelet neural networks are used by K. Vinay Kumaret al.11 to estimate effort. An improved FLANN algorithm for software work prediction was presented by B. Tirimula Rao et al. For the purpose of forecasting development costs and timelines, Multilayer perceptron and back propagation are used by G. Witting and G. Finnie4 as learning methods. algorithms. N. Karunanitthi et al. shown how to use ann to gauge the dependability of software that makes use of several feed- forward and Jordan network techniques. The training method for ann with back propagation is used by N. Tadayon14. A training set of data and a validation set of   data were created from the same data set, however this was not stated explicitly in the literature. Our goal in this vision is to use artificial neural network approaches to improve the five scaling variables for the COCOMO II estimate model as well as a number of dependent variables, including the cost drivers. It is difficult to comprehend the findings because neural networks, which act as "black boxes," have dispersed knowledge [5]. A neuro-fuzzy was suggested by Huang et al. to get past this restriction. Utilizing a cost model that is constructive and easy to understand to estimate software work [12]. A neuro-fuzzy technique, which has a great generalisation capacity and performs well with ambiguous and incorrect inputs, is the basis of this model. In simulations, the neuro-fuzzy model performed better for estimating software effort than the conventional COCOMO method [12].As we can see, there has been a lot of study on using decision tree-based models to improve SDEE accuracy. An algorithm for back-propagation learning is a type of artificial neural network. An ANN with a back-propagation algorithm is one of the most important learning algorithms in use right now. The issue of multiclass classification can also be solved with it. The hidden, output, and input layers make up an majority of ANNs. The nonlinear activation function argument, or sigmoid function, is specified by the weighted sum of the input neurons (Baareh et al., 2006). Let y1(p), y2(p),, yn(p) be the network's needed output and x1(p), x2(p),..., xn(p) be its inputs. P establishes the iteration number. El-Sayyad et al. (2015) provide the following illustration of the back-propagation neural network's function.

III. PROPOSED METHOD

The dataset is preprocessed so that effort may be calculated using a specific method and a limited number of equations. The estimated effort is then compared to the anticipated effort that was acquired by applying: Backpropagation, as well as the actual effort that was attained (Backpropagation). The created data mining models and method are then evaluated using MMRE, MRE and PRED Performance Evaluation Metrics (x).

A. Dataset (Desharnais)
Jean-Marc Desharnais produced this dataset in 1988 [4]. It is one of the earliest datasets for SDEE. It has so been utilized in numerous empirical research, including [10] [7] [9]. Dataset with 81 rows and 12 attributes which is of real software projects data from a software company called canadian make up this dataset. Based on their technological environments, these 81 projects have been divided into three subgroups: the conventional environment (46 projects), the "improved" traditional environment (25 projects), and the micro environment (10 projects). There are 12 features total in each project, nine of which (Team Experience, Manager Experience, Length(months), Entities, Transactions, Adjustment, Points Non Adjust, , Points Adjust, and Language(1,2,3)) are independent, and one of which (Effort) is dependent.

B.  Pre-processing of Data and Labelling

Preparing raw data to be used with a machine learning model is known as data pre-processing. In order to build a machine learning model, it is the first and most important stage. It is necessary to perform these actions in order to clean the data and prepare it for a machine learning model, which also improves the model's efficacy and accuracy.

There are ten attributes total in the dataset, one of which is effort, which is a dependent attribute. The remaining nine qualities are independent. First, the dataset is divided in half, with training data going into testing data in a ratio of 70: 30. The training data make up 30% of the dataset, while testing data make up 70% of it.
While testing the new instance, the effort attribute is removed from the dataset. The effort is then generated by the algorithm and verified against the actual effort values to ascertain the algorithm's correctness and the possible range of values for a particular set of project-related attributes.

???????C.  Back Propagation Algorithm

Feedforward neural networks are trained using the popular back propagation algorithm. Instead of explicitly computing the gradient with respect to each individual weight as would be the case naively, Using the network weights as input, it calculates the gradient of the loss function and is incredibly effective. Due to their effectiveness, gradient methods—including the popular gradient descent and stochastic gradient descent—can be used to update weights and train multi-layer networks to minimize loss. When using back propagation algorithm, the loss function's gradient with respect to each weight is computed using the chain rule, layers by layers, and reiterating the previous iteration layer in order to prevent the computation of intermediate terms in the chain rule from being repeated.

V. FUTURE ENHANCEMENT

Future editions of this model will incorporate the suggested model along with some tuning strategies to deal with values that are accompanied by confusing and unclear facts. expanding it to a strategy that is likely to be a future study area Future synthetic data generated using well-known approaches will be used to train and evaluate the suggested model, resulting in increased accuracy over current methods. By fine-tuning hyper parameters and using optimization techniques, the performance of the model can be improved. This will result in a successful software development project using estimating approaches, therefore in the future, the estimation of the effort in development of software projects can be done by using other important attributes.

Conclusion

This system designed is based on various neural network techniques that have been used to effort estimation. Every technique aims to provide the most accurate software effort estimation. In this research, we suggest that a good method for calculating software development effort is the back propagation algorithm, which is an ANN model. It was advised to adopt the back propagation strategy, which will quickly propagate errors, for complex and computationally intensive tasks, where the outcomes of the multilinear regression were contrasted however, it is necessary to assess the approaches\\\' correctness since they are mostly needed in software work estimation. We found that neuron-based models are more accurate estimators and can therefore be utilized to determine software effort estimation for all types of projects.

References

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Copyright

Copyright © 2023 Venkatesh A, Pavan , Santosh , Yugandhar G, Sunil Manoli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.