Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory

By Predescu, Eduard-Florin; Tefan, Alexandru et al. | Informatica Economica, April 1, 2019 | Go to article overview

Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory


Predescu, Eduard-Florin, Tefan, Alexandru, Zaharia, Alexis-Valentin, Informatica Economica


(ProQuest: ... denotes formula omitted.)

1 Introduction

In this paper our aim is to prove the effectiveness of machine learning in the planning and execution of a vast amount of projects. Machine learning, is a process of intense data analysis that concludes with the automation of analytical model building. It is a branch derived from artificial intelligence which consists of the fact that systems can learn from analyzed data, recognize patterns and make calculated decisions with minimal or no human interaction needed.

The challenge that this paper undertakes is to indicate which of the two machine learning algorithms studied MLP or LSTM is more efficient in the field of the machine learning in project management. The direct result of the aforementioned algorithms is to simplify the tasks of managers, to produce more precise predictions and finally and perhaps most importantly to increase sales and efficiency within the target company.

The primary challenge that most project managers face is the achieving of project goals within some different given constraints. Typically, this include but are not limited to, estimating a precise time frame in which the project will be completed which can be one of the trickiest parts of the job. Machine learning algorithms offer a simple solution to that conundrum as they can make project management simpler and more efficient. Different machine learning frameworks offer different benefits based on the particular field that they are being developed for.

Some of the advantages of machine learning are as follows:

* Predict and assign tasks to team members based on their skills;

* Predict and determine when deadlines aren't going to be met and what can be done to prevent it;

* Correct task time estimates as a means to update the project information and notify the manager that budget and time frames may require extension.

If implemented correctly and trained accordingly a machine learning software can reach a level of automation in which it automatically tracks and detects different problems and delays caused by various unexpected factors. It can also automatically detect delays within a project and act according to what the situation demands. This way the industry would see a total shift in the way projects are run and executed and this should lead to a positive impact on overall team performance and efficiency. Based on the evolutionary trajectory of machine learning algorithms in project management they will fundamentally change the way we as people think about the running and execution of projects, the challenges that arise on the way, in the form of technology and software limitations is something that must be considered very thoroughly. The implications of undertaking the implementation of machine learning algorithms in a large-scale legacy project can prove to be and insurmountable obstacle if we are to consider the following problems:

Hardware and software expenses, the amount of money required to be invested in both software development and hardware to train a software product can reach a staggering amount, due to the fact that machine learning algorithms are developed and trained with a particular objective in mind and quality of the required hardware components. While not necessarily a barring problem for big corporations with huge profit numbers and ability to invest in such a technology, it is still a time consuming operation which if not done correctly can lead to serious problems down the road.

We aim to achieve this goal by implementing and testing two machine-learning algorithms on the Desharnais dataset: Multi Layered Perceptron (MLP) and Long Short Term Memory (LSTM). In the following sections we will cover the aforementioned machine learning algorithms:

The sections that will follow this part of our study aim to provide and analyze real, tangible results. This will be realized though the construction of a machine learning system that will take as input data from a variety of real life projects, and will return the best possible result aimed at improving the planning and execution of similar projects. …

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