An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Subramanian, Girish H.; Pendharkar, Parag C.; Wallace, Mary

doi:10.1007/s10664-006-9023-3

Cited by 22 publications

(17 citation statements)

References 47 publications

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“…A wide range of techniques are being employed for effort estimation like Analogy based estimation [3], estimation by expert [4], rule induction techniques [5], algorithmic techniques with empirical strategies [6], artificial neural network designed methods [7,8,9], decision tree based strategies [10], Bayesian network techniques [11] and fuzzy logic based estimation structures [12] and ad-hoc approach based [13].…”

Section: A Effort Estimationmentioning

confidence: 99%

Machine Learning Application to Improve COCOMO Model using Neural Networks

Goyal¹,

Parashar²

2018

IJITCS

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Abstract-Millions of companies expend billions of dollars on trillions of software for the development and maintenance. Still many projects result in failure causing heavy financial loss. Major reason is the inefficient effort estimation techniques which are not so suitable for the current development methods. The continuous change in the software development technology makes effort estimation more challenging. Till date, no estimation method has been found full-proof to accurately pre-compute the time, money, effort (man-hours) and other resources required to successfully complete the project resulting either over-estimated budget or under-estimated budget. Here a machine learning COCOMO is proposed which is a novel non-algorithmic approach to effort estimation. This estimation technique performs well within their pre-specified domains and beyond so. As development methods have undergone revolutionaries but estimation techniques are not so modified to cope up with the modern development skills, so the need of training the models to work with updated development methods is being satiated just by finding out the patterns and associations among the domain specific data sets via neural networks along with carriage of desired COCOMO features. This paper estimates the effort by training proposed neural network using already published data-set and later on, the testing is done. The validation clearly shows that the performance of algorithmic method is improved by the proposed machine learning method.

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Section: A Effort Estimationmentioning

confidence: 99%

Machine Learning Application to Improve COCOMO Model using Neural Networks

Goyal¹,

Parashar²

2018

IJITCS

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“…Software effort estimation is a key consideration to software cost estimation [5]. There are numerous Software Effort Estimation Methods such as Algorithmic effort estimation, machine learning, empirical techniques, regression techniques and theory based techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Various models have been discussed in previous researches. An important task in software project management is to understand and control critical variables that influence software effort [5]. The paper by K.Smith, et.al.…”

Section: Related Workmentioning

confidence: 99%

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Software Effort Prediction using Statistical and Machine Learning Methods

Malhotra¹,

Jain²

2011

IJACSA

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-Accurate software effort estimation is an important part of software process. Effort is measured in terms of person months and duration. Both overestimation and underestimation of software effort may lead to risky consequences. Also, software project managers have to make estimates of how much a software development is going to cost. The dominant cost for any software is the cost of calculating effort. Thus, effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. There are various effort estimation models, but it is difficult to determine which model gives more accurate estimation on which dataset. This paper empirically evaluates and compares the potential of Linear Regression, Artificial Neural Network, Decision Tree, Support Vector Machine and Bagging on software project dataset. The dataset is obtained from 499 projects. The results show that Mean Magnitude Relative error of decision tree method is only 17.06%. Thus, the performance of decision tree method is better than all the other compared methods.

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“…A major aspect of internal quality is the code complexity, which directly affects the maintainability and defect proneness of code (Banker et al 1993;Subramanian et al 2006). Therefore, research interest on the topic of code complexity has been high over the years.…”

Section: Introductionmentioning

confidence: 99%

Evaluating code complexity triggers, use of complexity measures and the influence of code complexity on maintenance time

2017

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Code complexity has been studied intensively over the past decades because it is a quintessential characterizer of code's internal quality. Previously, much emphasis has been put on creating code complexity measures and applying these measures in practical contexts. To date, most measures are created based on theoretical frameworks, which determine the expected properties that a code complexity measure should fulfil. Fulfilling the necessary properties, however, does not guarantee that the measure characterizes the code complexity that is experienced by software engineers. Subsequently, code complexity measures often turn out to provide rather superficial insights into code complexity. This paper supports the discipline of code complexity measurement by providing empirical insights into the code characteristics that trigger complexity, the use of code complexity measures in industry, and the influence of code complexity on maintenance time. Results of an online survey, conducted in seven companies and two universities with a total of 100 respondents, show that among several code characteristics, two substantially increase code complexity, which subsequently have a major influence on the maintenance time of code. Notably, existing code complexity measures are poorly used in industry.

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An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Cited by 22 publications

References 47 publications

Machine Learning Application to Improve COCOMO Model using Neural Networks

Machine Learning Application to Improve COCOMO Model using Neural Networks

Software Effort Prediction using Statistical and Machine Learning Methods

Evaluating code complexity triggers, use of complexity measures and the influence of code complexity on maintenance time

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