Systematic mapping study of missing values techniques in software engineering data

Idri, Ali; Abnane, Ibtissam; Abran, Alain

doi:10.1109/snpd.2015.7176280

Cited by 22 publications

(31 citation statements)

References 31 publications

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“…Despite its simplicity, toleration is not a reliable approach, sometimes even providing estimates that are less efficient than estimation from deletion technique [8], [17]. b) Deletion technique Deletion is the most commonly used technique for dealing with missing data among researchers [8], [21]. It omits all cases with missing values from the analysis and only includes those cases for which all measurements are observed.…”

Section: ) Missigness Mechanismmentioning

confidence: 99%

Improved Analogy-based Effort Estimation with Incomplete Mixed Data

Abnane¹,

Idri²

2018

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

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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.

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Section: ) Missigness Mechanismmentioning

confidence: 99%

Improved Analogy-based Effort Estimation with Incomplete Mixed Data

Abnane¹,

Idri²

2018

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

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“…There are very few studies, focusing on missing data techniques and ABE together, however, missing data techniques have been studied on the software engineering datasets in general quite extensively. Idri, et al [28] performed a systematic mapping study on missing data and software engineering datasets. It was found in their study that Missing Data Imputation method was used the most out of the three methods for dealing with missing data.…”

Section: Related Workmentioning

confidence: 99%

MINN: A Missing Data Imputation Technique for Analogy-based Effort Estimation

Shah¹,

Dayang²,

Adham³

et al. 2019

ijacsa

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Success and failure of a complex software project are strongly associated with the accurate estimation of development effort. There are numerous estimation models developed but the most widely used among those is Analogy-Based Estimation (ABE). ABE model follows human nature as it estimates the future project's effort by making analogies with the past project's data. Since ABE relies on the historical datasets, the quality of the datasets affects the accuracy of estimation. Most of the software engineering datasets have missing values. The researchers either delete the projects containing missing values or avoid treating the missing values which reduce the ABE performance. In this study, Numeric Cleansing (NC), K-Nearest Neighbor Imputation (KNNI) and Median Imputation of the Nearest Neighbor (MINN) methods are used to impute the missing values in Desharnais and DesMiss datasets for ABE. MINN technique is introduced in this study. A comparison among these imputation methods is performed to identify the suitable missing data imputation method for ABE. The results suggested that MINN imputes more realistic values in the missing datasets as compared to values imputed through NC and KNNI. It was also found that the imputation treatment method helped in better prediction of the software development effort on ABE model.

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“…18,21 It has been frequently used in many fields, in particular in software engineering. 20 According to the SMS of Idri et al, 20 KNN imputation was by far the most used imputation techniques with 63% of selected studies. This is due to its advantages in terms of ease of implementation and use.…”

Section: Knn Imputation Techniquementioning

confidence: 99%

“…Imputation uses available data to impute the missing ones taking into consideration the type of missingness mechanisms . It has been frequently used in many fields, in particular in software engineering …”

Section: Introductionmentioning

confidence: 99%

“…Thereby, a proper handling of MD is necessary when analyses are performed in a domain, such as empirical software engineering, where accuracy and precision are key factors. 20 To examine the research on the use of MD techniques in software engineering datasets, Idri et al have conducted a mapping study in which 35 papers concerning MD treatments of software engineering data were selected. 20 They found that (1) imputation techniques were the most investigated techniques (97% of selected studies) compared with deletion (29% of selected studies) and toleration (11% of selected studies); and (2) k-nearest neighbor (KNN) imputation was by far the most used imputation techniques with 63% of selected studies.…”

Section: Introductionmentioning

confidence: 99%

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Support vector regression‐based imputation in analogy‐based software development effort estimation

Idri

Abnane

Abran

2018

J Software Evolu Process

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Missing data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort estimation (SDEE) techniques. To deal with this challenge, several imputation techniques have been investigated in SDEE and k‐nearest neighbors (KNN)‐based imputation is still the most frequently used. To the best of our knowledge, no study has used support vector regression (SVR)‐based imputation to construct accurate estimation techniques, in particular those based on analogy. This paper introduces a new imputation technique based on SVR for handling MD in two analogy‐based SDEE techniques: classical analogy and fuzzy analogy. More specifically, we investigate whether the use of SVR instead of KNN in imputing MD improves the predictive performance of these two analogy‐based techniques. A total of 1134 experiments were conducted involving seven datasets, SVR/KNN MD imputation techniques (KNN with Euclidean and Manhattan distances), three missingness mechanisms (missing completely at random, missing at random, non‐ignorable missing), and MD percentages from 10% to 90%. The results suggest that the use of SVR imputation, rather than KNN imputation, may improve the prediction performance of both analogy‐based techniques. Furthermore, we found that the impact of MD percentage upon effort prediction performance is reduced when using SVR rather than KNN. Moreover, fuzzy analogy generates better estimates in terms of the standardized accuracy measure than classical analogy regardless of the MD technique, the dataset used, the missingness mechanism, or the MD percentage.

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Systematic mapping study of missing values techniques in software engineering data

Cited by 22 publications

References 31 publications

Improved Analogy-based Effort Estimation with Incomplete Mixed Data

Improved Analogy-based Effort Estimation with Incomplete Mixed Data

MINN: A Missing Data Imputation Technique for Analogy-based Effort Estimation

Support vector regression‐based imputation in analogy‐based software development effort estimation

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