Software development effort estimation: a systematic mapping study

Carbonera, Carlos Eduardo; Farias, Kleinner; Bischoff, Vinícius

doi:10.1049/iet-sen.2018.5334

Cited by 27 publications

(21 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We can relieve the shortcomings, which may lead to a more accurate estimation that can be acquired from single models. 46,47 There are two types of EEE methods 48 :…”

Section: Outline Of An Ensemble Technique and Accuracy Evaluation Metricsmentioning

confidence: 99%

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

et al. 2021

View full text Add to dashboard Cite

Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule.The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non-publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state-of-the-art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.

show abstract

“…We can relieve the shortcomings, which may lead to a more accurate estimation that can be acquired from single models. 46,47 There are two types of EEE methods 48 :…”

Section: Outline Of An Ensemble Technique and Accuracy Evaluation Metricsmentioning

confidence: 99%

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Thus, a total of 35, 38, 48, 47, 34, 51, and 34 SLR equations of type ln (CE) = a + b * ln (FSM) were generated by data set. Each AR and the mean of ARs are calculated using Equations (12) and ( 13), respectively.…”

Section: Training and Testing The Modelsmentioning

confidence: 99%

“…I did not identify any study on software effort prediction models whose objective has been the application of a SVR-SMO-PUK. 3,6,7,11,12,20,[34][35][36][37] 3. I identified nine studies that apply SVR to effort prediction.…”

mentioning

confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

View full text Add to dashboard Cite

The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

show abstract

“…Software effort estimation is the process of predicting the most realistic amount of effort required to develop or maintain software based on incomplete, uncertain, and noisy data [22]. Software is the most expensive component in many computer-based systems that affects a number of bugs that produce enormous differences between gain and loss during effort estimation [23].…”

Section: Software Effort Estimationmentioning

confidence: 99%

“…It was proposed by Barry Boehm and is based on the study of 63 projects, making it one of the bestdocumented models [30]. Although COCOMO is among the most common, COCOMO seems to become less accurate due to increased complexity and over-demanding software requirements [23] [31]. COCOMO model uses a regression formula based on a number of lines of code (LOC).…”

Section: Cocomo Modelmentioning

confidence: 99%

Optimized COCOMO parameters using hybrid particle swarm optimization

Zakaria

Ismail

Abidin

et al. 2021

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

show abstract

Software development effort estimation: a systematic mapping study

Cited by 27 publications

References 56 publications

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

Effort prediction for the software project construction phase

Optimized COCOMO parameters using hybrid particle swarm optimization

Contact Info

Product

Resources

About