Using Classification Trees for Software Quality Models: Lessons Learned

Khoshgoftaar, Taghi M.; Allen, Edward B.; Naik, A.; Jones, Wendell; Hudepohl, J.P.

doi:10.1142/s0218194099000140

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…If true, the extensive research on reliability statistical models (e.g. [16,22]) that have been shown to predict fault-and failure-prone components early in the SLC may also be helpful for security prediction models. The models can be modified to isolate security problems, or if we assume that the security faults cluster with the non-security faults, then security engineers can focus their efforts to the components predicted to be the most failure-prone by the reliabilitybased prediction model.…”

Section: Limitationsmentioning

confidence: 99%

Toward Non-security Failures as a Predictor of Security Faults and Failures

Gegick

Rotella

Williams

2009

Lecture Notes in Computer Science

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Abstract. In the search for metrics that can predict the presence of vulnerabilities early in the software life cycle, there may be some benefit to choosing metrics from the non-security realm. We analyzed non-security and security failure data reported for the year 2007 of a Cisco software system. We used non-security failure reports as input variables into a classification and regression tree (CART) model to determine the probability that a component will have at least one vulnerability. Using CART, we ranked all of the system components in descending order of their probabilities and found that 57% of the vulnerable components were in the top nine percent of the total component ranking, but with a 48% false positive rate. The results indicate that nonsecurity failures can be used as one of the input variables for security-related prediction models.

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Section: Limitationsmentioning

confidence: 99%

Toward Non-security Failures as a Predictor of Security Faults and Failures

Gegick

Rotella

Williams

2009

Lecture Notes in Computer Science

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“…1 There are few prior applications of logistic regression to software T. M. Khoshyoftaar & E. B. Allen quality models in the literature, 2 ' 3 and none that we know of that account for prior probabilities and costs of misclassification. Previous software quality modeling classification studies by other researchers have used uniform prior probabilities and equal costs for all kinds of misclassifications.…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Modeling of Software Quality

Khoshgoftaar

Allen

1999

Int. J. Rel. Qual. Saf. Eng.

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101

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Reliable software is mandatory for complex mission-critical systems. Classifying modules as fault-prone, or not, is a valuable technique for guiding development processes, so that resources can be focused on those parts of a system that are most likely to have faults.Logistic regression offers advantages over other classification modeling techniques, such as interpretable coefficients. There are few prior applications of logistic regression to software quality models in the literature, and none that we know of account for prior probabilities and costs of misclassification. A contribution of this paper is the application of prior probabilities and costs of misclassification to a logistic regressionbased classification rule for a software quality model. This paper also contributes an integrated method for using logistic regression in software quality modeling, including examples of how to interpret coefficients, how to use prior probabilities, and how to use costs of misclassifications. A case study of a major subsystem of a military, real-time system illustrates the techniques.

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“…In this paper, we apply our decision rule at each leaf to predict the class of the object. This paper confirms prior work [13,15,20,25,301 showing that classification trees can be useful to identify fault-prone modules based on the pattern of software metrics and furthermore, explores whether process metrics and execution metrics can be significant predictors, in addition to product metrics, when modeling with regression trees and our classification rule.…”

supporting

confidence: 86%

“…Prior work with classification trees in software engineering has explored several tree-building algorithms [28, 301. Our research group has classified fault-prone modules with the Classification And Regression Trees (CART) algorithm [21] and the TREEDISC algorithm [22] which is a refinement of the CHAID algorithm. The S-Plus package also has an algorithm for constructing classification trees [3].…”

mentioning

confidence: 99%

Using product, process, and execution metrics to predict fault-prone software modules with classification trees

Khoshgoftaar

Shan

Allen

Proceedings. Fifth IEEE International Symposium on High Assurance Systems Engineering (HASE 2000)

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Software-quality classification models can make predictions to guide improvement efforts to those modules that need it the most. Based on software metrics, a model can predict which modules will be considered fault-prone, or not. In this paper, we consider a module fault-prone if any faults were discovered b y customers. Useful predictions are contingent on the availability of candidate predictors that are actually related t o faults discovered b y customers. With a diverse set of candidate predictors in hand, classification-tree modeling as a robust technique for building such software quality models.This paper presents an empirical case study of four releases of a very large telecommunications system. The case study used the regression-tree algorithm in the S-Plus package .and then applied our general decision rule to classify modules. Results showed that in addition to product metrics, process metrics and execution metrics were significant predictors of faults discovered b y customers.

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Using Classification Trees for Software Quality Models: Lessons Learned

Cited by 18 publications

References 18 publications

Toward Non-security Failures as a Predictor of Security Faults and Failures

Toward Non-security Failures as a Predictor of Security Faults and Failures

Logistic Regression Modeling of Software Quality

Using product, process, and execution metrics to predict fault-prone software modules with classification trees

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