Data Mining Static Code Attributes to Learn Defect Predictors

Menzies,; Greenwald,; Frank, Stephanie

doi:10.1109/tse.2007.256941

Cited by 1,192 publications

(1,147 citation statements)

References 33 publications

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“…In addition the balance measure which was defined by Menzies et.al as a measure of the distance from a point on the receiver operating characteristic (ROC) curve to the ideal point. Which is typically defined as where the true positive rate is 1 and the false positive rate is 0 [1].…”

Section: B Useful Statistical Datamentioning

confidence: 99%

“…3. In the domain of software defect prediction, often the data sets under study represents less than 1 percent of the data point in total [1], [10] and [11]. Referencing [5] presented an example of using the most imbalanced of the NASA Metric Data Program; PC2.…”

Section: A Analysis Of Precision Measurementioning

confidence: 99%

“…In the January, 2007 issue of the IEEE Transactions on Software Engineering Journal, a paper with the title "Data Mining Static Code Attributes to Learn Defect Predictors" [1], was published. The authors of [1] carried out many defect prediction experiments.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [1] carried out many defect prediction experiments. The paper referred to two metrics, namely the probability of detection (pd) or recall and the probability of false alarm (pf).…”

Section: Introductionmentioning

confidence: 99%

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A Deep Analysis of the Precision Formula for Imbalanced Class Distribution

Armah¹,

Luo²,

Qin³

2014

IJMLC

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Abstract-In this paper we discussed our proposed formula of precision for an imbalanced class distribution which gives a true reflection of a defect predictor in relation to its high classifier performance. Our formula gave values which were closer to the Accuracy computation for both balanced and imbalanced class distribution, thus our formula gave consistent high values for a good predictor irrespective of the size of our target data.Approach: We used NASA dataset to come out with well-documented examples as to how to get a higher accuracy, with its corresponding higher precision and subsequently a higher Recall and F-Measure values which are reflection of the higher classifier performance. We used data with the minority class between 5 to 10 percent (5%-10%) data points inclusive. We applied a fixed true positive rate (TPR) of one (1), whiles the false positive rate (FPR) on the other hand ranged from 0.01 to 0.05 inclusive at an interval of 0.01 for our analysis .We used the proposed adjusted formula for precision computation to improve earlier works which were criticized of not being satisfactory. The proposed formula precision (AR) was used to compute the precisions which gave results that were the true reflection of a higher performance predictor. The results in the tables clearly show our assertion for our formula giving good estimated values for precision.Index Terms-True positive rate, false positive rate, precision, recall, F-measure and defect predictor classifier.

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Section: B Useful Statistical Datamentioning

confidence: 99%

Section: A Analysis Of Precision Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Analysis of the Precision Formula for Imbalanced Class Distribution

Armah¹,

Luo²,

Qin³

2014

IJMLC

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show abstract

“…The process of defect prediction has been utilizing various machine learning approaches including Logistic Regression [6], Decision Trees [7], Neural Networks [8] and Naive-Bayes [9]. The two important data quality aspects such as class imbalance and noisy data set attributes [10] generally influence the performance of classification.…”

Section: Introductionmentioning

confidence: 99%