Improved Analogy-based Effort Estimation with Incomplete Mixed Data

Abstract

Estimation by analogy (EBA) is one of the most attractive software effort development estimation techniques. However, one of the critical issues when using EBA is the occurrence of missing data (MD) in the historical data sets. The absence of values of several relevant software attributes is a frequent phenomenon that may cause inaccurate EBA estimations. The MD can be numerical and/or categorical. This paper evaluates four MD techniques (toleration, deletion, k-nearest neighbors (KNN) imputation and support vector regression (SVR) imputation) over four mixed data sets. A total of 432 experiments were conducted involving four MD techniques, nine MD percentages (from 10% to 90%), three missingness mechanisms (MCAR: Missing Completely at Random, MAR: Missing at Random and NIM: Non-Ignorable Missing) and four data sets. The evaluation process consists of four steps and uses several accuracy measures such as standardized accuracy (SA) and prediction level (Pred).The results suggest that EBA with imputation techniques achieved significantly better SA values over EBA with toleration or deletion regardless of the mechanism of missingness. Moreover, no particular MD imputation technique outperformed the other techniques overall. However, according to Pred and other accuracy criteria, EBA with SVR was the best, followed by KNN imputation; we also found that toleration instead of deletion improves the accuracy of EBA.

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

“…The results shown that SVR imputation is less sensitive regarding MD percentage compared to KNN imputation. The results confirmed that for both SVR imputation and KNN imputation had worse performance under MNAR mechanism compared to both MAR and MCAR [43]. Numerical and categorical MD toleration, deletion, KNNI, SVR imputationMAR MCAR MNARFindingThe results confirmed that imputation techniques achieved better accuracy improvements compared to toleration and deletion.…”

“…Idri, Abnane et al[42] proposed SVR (Support Vector Regression) imputation, empirical results indicated that SVRI outperformed KNNI under different missing ratio and MD mechanisms for ABE model. Abnane and Idri[43] investigated mixed (Numerical and categorical) MD imputation techniques for ABE model, imputation techniques achieved better accuracy results, there is no significant difference between SVR and KNNI for mixed MD imputation. Muhammad Arif Shah[44] proposed Median Imputation of the Nearest Neighbor (MINN) for ABE mode , the investigation of the proposed model under Desharnais dataset outperformed both MI and KNN under MNAR mechanism.Abnane, Hosni et al[45] optimize parameters of KNN imputation using grid search, the optimize KNN imputation improved ABE significantly compared with regular KNN imputation.…”

Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimation

“…KNN imputation (KNNI) is widely used to deal with the MD issue due to its simplicity, popularity, and usefulness. 2,5,6,11 KNNI, which is based on case-based reasoning, computes the similarity between an incomplete project and complete projects by using a distance function and then uses the most similar projects to impute the MD.…”

Analysis of cluster center initialization of 2FA‐kprototypes analogy‐based software effort estimation

“…Therefore, feature subset selection techniques should be applied to find the optimal set of features . Another important limitation of ASEE techniques is their inability to handle missing values …”