A Double-Weighted Parametric Model for Academic Software Project Effort Estimation

Saini, Jatinderkumar R.; Chomal, Vikas S.

doi:10.1007/978-981-15-0630-7_4

Cited by 3 publications

(2 citation statements)

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“…A broad spectrum of techniques exists in the public domain to assess & estimate the effort required to test software [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]. However, none of the literature cited includes any viable technique suitable to the early stages of commercial engagement.…”

Section: Client-side Issuesmentioning

confidence: 99%

An experimentally verified method to predict & measure all forms of Cyber-Risk

Storti¹

2021

Preprint

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A precise & unambiguous mathematical definition of Cyber-Risk is developed, yielding an experimentally validated solution demonstrating ‘How to Predict & Measure Cyber-Risk’ for any Internet Connected Information System (ICIS) to greater than 98.07% accuracy. Moreover, it is shown that the solution holds for all scales of ICIS, from an Application level to an Enterprise level. In addition, it is shown that Test Effort Estimation (TEE) quantifies Cyber-Confidence, which in turn quantifies Cyber-Risk. Hence, TEE is a Mission Critical Activity (MCA) when formulating Cyber-Risk Management Strategies & may be utilised prior to project commencement, in-flight or post facto as an assessment &/or auditing tool. The TEE Model Construct developed is a statistical based methodology whereby the evaluations/decisions made, result in the contraction or expansion of the ‘z-Score’ associated with an infinite population of database records. The primary advantage of this approach is that very little information is required client-side at the engagement stage in order to produce peer acceptable estimates of the required test effort, & to accurately predict & measure the associated Cyber-Risk. This approach empowers clients & service providers to precisely define whatever level of Cyber-Risk is to be contractually delivered, capable of being absorbed, or prepared to be absorbed by consensus. With the aid of a decision table, estimators are able to articulate & convey to the appropriate authorities, various levels of Cyber-Risk commensurate with the available resources. The TEE Model Construct developed, presents an experimentally verified methodology, cognizant of commercial realities, yielding the following key advantages; (i) it requires minimal inputs, (ii) it has a scientific foundation, (iii) it facilitates operational decision-making, (iv) it quantifies Risk Based Testing (RBT), (v) it is simple, robust, flexible, consistent, reusable & transparent, (vi) it is capable of scaling a projected solution from a known solution, (vii) it embraces Continuous Improvement Processes (CIP’s), (viii) it confines perceptual subjectivity predominantly to three variables & (ix), it commercially exists as an off-the-shelf product. <br>

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Section: Client-side Issuesmentioning

confidence: 99%

An experimentally verified method to predict & measure all forms of Cyber-Risk

Storti¹

2021

Preprint

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“…Chatzoglou and Macaulay [9] in their research work proposed and developed a new model called MARCS which was used to provide predictions for resources required during software development process. Additionally, a team-building model for the software projects [33] as well as effort estimation models [34] [35] have also been proposed by the researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

On Effort Distribution in Software Project Development for Academic Domain

Saini,

Chomal

2020

IJEAT

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Effort distribution in software engineering is a well-known term used to measure cost and effort estimation for each and every phase or activity in software development. Effort distribution is taken in consideration in almost all IT companies while developing software. But it is mostly not considered or overlooked in developing academic software projects by students of computer science courses. The paper presents with results of an experimentation on phase effort distribution data of 84 software academic projects of post graduate final year students of computer science. The phase effort distribution provided by students were collected, analyzed and compared with COCOMO II model which provides effort distribution required in software development. Finally, this paper also discusses and provides recommendation about the use and importance of effort distribution in academic software projects development.

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