A model for software development effort and cost estimation

“…Many software effort/cost estimation models where proposed to help in providing a high quality estimate to assist a project manager in considering the best decisions for a project [8], [9]. Many software cost estimation models were reported in the literature [10]- [13]. These models were used to help project managers to estimate effort, time and cost.…”

Section: • Inadequate Financial Resourcesmentioning

confidence: 99%

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

Sheta¹,

Kassaymeh²,

Rine³

2014

ijacsa

View full text Add to dashboard Cite

Abstract-On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

show abstract

“…The computer facilities represent the computer time, which is necessary for failure detection and rectification. The influence of software testing effort has been included in several SRGMs Putnam [30]; Pillai and Nair [23]; Kapur, Garg, and Kumar [18]; Xie [26]; Ichimori, Yamada, and Nishiwaki [11]; Kapur and Bardhan [16]; Huang et al [9]; Kapur et al [19], Myers [21] planned that software will be constructed and tested individually in a chronological step. Yamada et al [27], Hou, Kuo, and Chang [7], Pham and Zhang [22] have recommended that system level software testing occurred only after the system was completely developed.…”

Section: Research Backgroundmentioning

confidence: 99%

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Johri¹,

Nasar²,

Chanda³

2013

IJCA

View full text Add to dashboard Cite

Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters. General Terms Evolutionary Algorithm KeywordsGenetic Algorithm, testing effort allocation, Software reliability, SRGM Nomenclature a is the initial fault content in the software. b is the fault detection rate.is the number of fault removed at time 't'. m(t) is the cumulative number of fault detected till time 't' due to the testing effort w1(t).T the planning period.C1(m(t),w2(t)) Cost per unit at time 't' for cumulative faults removed m(t) and debugging effort w2(t).C 2 is the cost of testing per unit testing efforts.W is the total resources utilized during the SDLC at any point of time 't'.W 1 (t) current effort expenditure due to testing at time 't' W 2 (t) current effort expenditure due to debugging the fault at time 't'

show abstract

A model for software development effort and cost estimation

Cited by 147 publications

References 10 publications

Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators

Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Contact Info

Product

Resources

About