Duplex output software effort estimation model with self-guided interpretation

Mensah, Solomon; Keung, Jacky; Bosu, Michael Franklin; Bennin, Kwabena Ebo

doi:10.1016/j.infsof.2017.09.010

Cited by 42 publications

(67 citation statements)

References 48 publications

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“…Firstly, accuracy was checked by running frequency tables to have a general feel to the data. At this point some values were found to be missing [12]. From the charts below, sub-systems are seen to have recorded more neutral values than the other variables.…”

Section: Discussionmentioning

confidence: 89%

“…Data was transformed and recoded after which there were 162 valid respondents remaining. Thereafter, constructs were grouped into their respective variables [12]. Thirdly, a Cronbach's alpha test was run to check the reliability of the scale used.…”

Section: Discussionmentioning

confidence: 99%

“…Homogeneity was confirmed because results of the regression standardized residual centred around zero. Homoscedasticity was however not confirmed because the dots did not form a blob [12]. Finally, correlation among variables was examined using the Pearson's correlation test [15,16].…”

Section: Moderation and Moderated Mediation Analysismentioning

confidence: 99%

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Flash Flood Warning Sub-Systems for Rural Africa

Mbau¹,

Thiruchelvam²

2018

IJET

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This paper aims to present the need for sub-systems in rural Africa for real-time warning delivery. It has been reported in previous studies, that Sub-Sahara Africa lacks weather radars. This means that there are no real-time early warnings presenting a gap in knowledge that this study aims to address. This is done through the following objective; to examine the relationship between variables in the study and therefore, establish whether sub-systems are a significant variable in flash flood warning systems for rural Africa. The variables to be examined are; the independent variable (existing warning system), the dependent variable (early warnings), the moderator variable (ancillary elements) and the mediator variable (sub-systems). This is investigated through a closed-ended questionnaire that is administered to a sample of meteorologists whose email addresses are available on the World Meteorological Organization’s expert database. The target sample is determined through the G*Power application. The data is analyzed on SPSS. Variables in the study are found to be correlated after conducting a Pearson’s correlation test. Using PROCESS allows for the testing of various models where moderation is confirmed. A moderated mediation model is also confirmed. The results confirm that sub-systems are significant enough to be developed for rural Africa.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

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Flash Flood Warning Sub-Systems for Rural Africa

Mbau¹,

Thiruchelvam²

2018

IJET

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“…Cook's distance was used in the identification and treatment of influential data points during the model construction. In this study, we realized that, these influential data points identified yielded no negative effects on the models when the datasets were normalized using the z-score normalization technique as done in our previous study [2].…”

Section: B Data Preprocessingmentioning

confidence: 85%

“…Two classical SEE datasets have been used in the development of effort prediction models in this paper. We will adopt the same procedure employed in our previous paper [2] by generating three effort classes using the density quantile function.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Conclusion Instability Issue in Software Effort Estimation (S)

Bosu

Mensah

Bennin

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Conclusion instability is the absence of observing the same effect under varying experimental conditions. Deep Neural Network (DNN) and ElasticNet software effort estimation (SEE) models were applied to two SEE datasets with the view of resolving the conclusion instability issue and assessing the suitability of ElasticNet as a viable SEE benchmark model. Results were mixed as both model types attain conclusion stability for the Kitchenham dataset whilst conclusion instability existed in the Desharnais dataset. ElasticNet was outperformed by DNN and as such it is not recommended to be used as a SEE benchmark model.

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A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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Duplex output software effort estimation model with self-guided interpretation

Cited by 42 publications

References 48 publications

Flash Flood Warning Sub-Systems for Rural Africa

Flash Flood Warning Sub-Systems for Rural Africa

Revisiting the Conclusion Instability Issue in Software Effort Estimation (S)

A classification scheme to improve conclusion instability using Bellwether moving windows

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