Privacy and utility for defect prediction: Experiments with MORPH

Peters, Jan; Menzies,

doi:10.1109/icse.2012.6227194

Cited by 58 publications

(54 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extends a prior publication [7] in five ways, First, as said above, this paper shows that combining CLIFF and MORPH is better than just running MORPH.…”

Section: Relation To Prior Publicationssupporting

confidence: 69%

“…Also, combining CLIFF and MORPH improves on prior results. Previously, Peters and Menzies [7] used MORPH and found that, sometimes, the privatized data exhibited worse performance than the original data. In this study, we combine CLIFF and MORPH and show that there are no significant reductions in the classification performance in any of the datasets we study.…”

Section: Results and Contributionsmentioning

confidence: 99%

“…The result of this is shown in Table 2b. For example, the attribute values of wmc are replaced by two subranges of values ( [3][4][5][6] and (6)(7)(8)(9)(10)(11)(12)(13)(14)). Here, all values from 3 to 6 inclusive are placed in the first subrange and all values between 6 and 14 (not including 6) are placed in the last subrange.…”

Section: Example Of Cliff+morphmentioning

confidence: 99%

“…3. Randomly select an attribute from A, for example, wmc with two possible subranges (6)(7)(8)(9)(10)(11)(12)(13)(14) and [0-6]. 4.…”

Section: Query Generatormentioning

confidence: 99%

“…Values at the leaves are generalized by replacing them with the subranges [3][4][5][6] or (6)(7)(8)(9)(10)(11)(12)(13)(14). These in turn can be replaced by [3][4][5][6][7][8][9][10][11][12][13][14]. Or the leaf values can be suppressed by replacing them with a symbol such as the stars at the top of the tree.…”

Section: Benchmark Privacy Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

Balancing Privacy and Utility in Cross-Company Defect Prediction

Peters

Gong

Zhang

2013

IIEEE Trans. Software Eng.

Self Cite

130

View full text Add to dashboard Cite

Abstract-Background: Cross-company defect prediction (CCDP) is a field of study where an organization lacking enough local data can use data from other organizations for building defect predictors. To support CCDP, data must be shared. Such shared data must be privatized, but that privatization could severely damage the utility of the data. Aim: To enable effective defect prediction from shared data while preserving privacy. Method: We explore privatization algorithms that maintain class boundaries in a dataset. CLIFF is an instance pruner that deletes irrelevant examples. MORPH is a data mutator that moves the data a random distance, taking care not to cross class boundaries. CLIFF+MORPH are tested in a CCDP study among 10 defect datasets from the PROMISE data repository. Results: We find: 1) The CLIFFed+MORPHed algorithms provide more privacy than the state-of-the-art privacy algorithms; 2) in terms of utility measured by defect prediction, we find that CLIFF+MORPH performs significantly better. Conclusions: For the OO defect data studied here, data can be privatized and shared without a significant degradation in utility. To the best of our knowledge, this is the first published result where privatization does not compromise defect prediction.

show abstract

“…This paper extends a prior publication [7] in five ways, First, as said above, this paper shows that combining CLIFF and MORPH is better than just running MORPH.…”

Section: Relation To Prior Publicationssupporting

confidence: 69%

Section: Results and Contributionsmentioning

confidence: 99%

Section: Example Of Cliff+morphmentioning

confidence: 99%

“…3. Randomly select an attribute from A, for example, wmc with two possible subranges (6)(7)(8)(9)(10)(11)(12)(13)(14) and [0-6]. 4.…”

Section: Query Generatormentioning

confidence: 99%

Section: Benchmark Privacy Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Balancing Privacy and Utility in Cross-Company Defect Prediction

Peters

Gong

Zhang

2013

IIEEE Trans. Software Eng.

Self Cite

130

View full text Add to dashboard Cite

show abstract

Do different cross‐project defect prediction methods identify the same defective modules?

Chen

Qu³

et al. 2019

J Software Evolu Process

View full text Add to dashboard Cite

Cross‐project defect prediction (CPDP) is needed when the target projects are new projects or the projects have less training data, since these projects do not have sufficient historical data to build high‐quality prediction models. The researchers have proposed many CPDP methods, and previous studies have conducted extensive comparisons on the performance of different CPDP methods. However, to the best of our knowledge, it remains unclear whether different CPDP methods can identify the same defective modules, and this issue has not been thoroughly explored. In this article, we select 12 state‐of‐the‐art CPDP methods, including eight supervised methods and four unsupervised methods. We first compare the performance of these methods in the same experiment settings on five widely used datasets (ie, NASA, SOFTLAB, PROMISE, AEEEM, and ReLink) and rank these methods via the Scott‐Knott test. Final results confirm the competitiveness of unsupervised methods. Then we perform diversity analysis on defective modules for these methods by using the McNemar test. Empirical results verify that different CPDP methods may lead to difference in the modules predicted as defective, especially when the comparison is performed between the supervised methods and unsupervised methods. Finally, we also find there exist a certain number of defective modules, which cannot be correctly identified by any of the CPDP methods or can be correctly identified by only one CPDP method. These findings can be utilized to design more effective methods to further improve the performance of CPDP.

show abstract

A soft computing approach for software defect density prediction

Azzeh

Alqasrawi

Elsheikh

2023

J Software Evolu Process

View full text Add to dashboard Cite

Defect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none‐defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty.

show abstract

Privacy and utility for defect prediction: Experiments with MORPH

Cited by 58 publications

References 32 publications

Balancing Privacy and Utility in Cross-Company Defect Prediction

Balancing Privacy and Utility in Cross-Company Defect Prediction

Do different cross‐project defect prediction methods identify the same defective modules?

A soft computing approach for software defect density prediction

Contact Info

Product

Resources

About