Missing Data Imputation Techniques for Software Effort Estimation: A Study of Recent Issues and Challenges

AlMutlaq, Ayman Jalal; Jawawi, Dayang Norhayati Abang

doi:10.1007/978-3-030-33582-3_107

Cited by 2 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…but is limited to practices in the software engineering (SE) domain. Almutlaq and Jawawi (2020) also conducted an evaluation on the existing data imputation techniques, issues and challenges in software effort estimation. Another review study (Lin and Tsai, 2020), similar to our study, provided statistical analyses of technical questions related to the experimental design like the missing ratio, domain, missing mechanism of the experimental data sets used in different literature during 2006–2017.…”

Section: Prior Workmentioning

confidence: 99%

A systematic review of machine learning-based missing value imputation techniques

Thomas

Rajabi

2021

DTA

View full text Add to dashboard Cite

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

show abstract

Section: Prior Workmentioning

confidence: 99%

A systematic review of machine learning-based missing value imputation techniques

Thomas

Rajabi

2021

DTA

View full text Add to dashboard Cite

show abstract

“…Huang, et al [6] Evaluated empirically data preprocessing techniques used for machine learning effort estimation models; the study validated missing data treatment techniques effectiveness to improve accuracy of prediction effort. Almutlaq and Jawawi [9] Reviewed recent missing data techniques in software effort estimation field, the study elaborated two major challenges that are imputation technique performance oriented and incomplete dataset oriented. www.ijacsa.thesai.org Strike, et al [5] Investigated three missing data techniques (deletion, mean imputation, and hot-deck imputation) with three missing mechanisms (MCAR, MAR,and NIM) on regression effort estimation model.it have been found that hotdeck imputation outperformed other methods.…”

Section: Step 1: Beginmentioning

confidence: 99%

“…Almutlaq and Jawawi [9], classified missing data imputation challenges for software effort estimation into two major categories, the categories are performance oriented and dataset challenges. Performance oriented challenges refers to challenges and issues that exist within the techniques itself on a performance level (missing data Accuracy, Model performance accuracy, and time efficiency).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

AlMutlaq¹,

Jawawi²,

Arbain³

2021

IJACSA

Self Cite

View full text Add to dashboard Cite

The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model.

show abstract

Missing Data Imputation Techniques for Software Effort Estimation: A Study of Recent Issues and Challenges

Cited by 2 publications

References 26 publications

A systematic review of machine learning-based missing value imputation techniques

A systematic review of machine learning-based missing value imputation techniques

Fuzzy C-mean Missing Data Imputation for Analogy-based Effort Estimation

Contact Info

Product

Resources

About