A hybrid faulty module prediction using association rule mining and logistic regression analysis

Kamei, Yasutaka; Monden, Akito; Morisaki, Shuji; Matsumoto, Kenichi

doi:10.1145/1414004.1414051

Cited by 25 publications

(12 citation statements)

References 7 publications

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“…The association rule mining has been proposed as a non-model based (rule-based) prediction method (Kamei et al, 2008). Unlike the regression models which require the dependent and independent variables to be clearly defined and consequently force the analyst to priorly well-defines the questions and hypotheses the association rule mining can extract relations among the variables for which no questions have been formulated.…”

Section: Spatial Association Rule Miningmentioning

confidence: 99%

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Karimipour¹,

Kanani‐Sadat²

2016

J ENVIRON INFORM

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ABSTRACT. The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus, and then avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this article, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. This investigation, eventually, leads to map the vulnerability of asthmatic allergy prevalence based on environmental characteristics. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The categories of attributes have been defined as fuzzy sets in order to handle the data uncertainty. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that. In June, however, the distance to parks and roads as well as NO2, PM10, and PM2.5 affect the allergy prevalence, but CO, SO2 and O3 are ineffective.

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Section: Spatial Association Rule Miningmentioning

confidence: 99%

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Karimipour¹,

Kanani‐Sadat²

2016

J ENVIRON INFORM

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“…This becomes a problem when matched rules had different consequents (faulty and not-faulty). A typical way to handle this situation is to classify the module by the majority of rules' consequent [7]. Another way is to select the longest rules among matched rules [22].…”

Section: B Fault-prone Module Prediction By Rulesmentioning

confidence: 99%

“…In such cases, Kamei et al proposed to conduct a modelbased prediction as a complementary method [7].…”

Section: B Fault-prone Module Prediction By Rulesmentioning

confidence: 99%

A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

Monden

Keung

Morisaki

et al. 2012

2012 19th Asia-Pacific Software Engineering Conference

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Abstract-Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only .015 (from .757 down to .742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from .426 to .441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

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“…In addition, they have been widely used in prediction models to determine how faulty classes will be during the testing phase, showing their ability to predict fault-proneness. [3]- [8] (see Fig. 1…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, they have been related to managerial factors such as productivity, re-work effort for reusing classes and design effort and maintenance effort [1], [2]. In addition, the CK metrics, when measured from the code, have shown their ability to predict fault-prone code in several empirical studies [3]- [8]. Most of the results of these studies have found that coupling measures, such as RFC, are strongly related to fault-proneness code.…”

Section: Introductionmentioning

confidence: 99%

A UML Approximation of Three Chidamber-Kemerer Metrics and Their Ability to Predict Faulty Code across Software Projects

Cruz

Ochimizu

2010

IEICE Trans. Inf. & Syst.

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SUMMARYDesign-complexity metrics, while measured from the code, have shown to be good predictors of fault-prone object-oriented programs. Some of the most often used metrics are the Chidamber and Kemerer metrics (CK). This paper discusses how to make early predictions of fault-prone object-oriented classes, using a UML approximation of three CK metrics. First, we present a simple approach to approximate Weighted Methods per Class (WMC), Response For Class (RFC) and Coupling Between Objects (CBO) CK metrics using UML collaboration diagrams. Then, we study the application of two data normalization techniques. Such study has a twofold purpose: to decrease the error approximation in measuring the mentioned CK metrics from UML diagrams, and to obtain a more similar data distribution of these metrics among software projects so that better prediction results are obtained when using the same prediction model across different software projects. Finally, we construct three prediction models with the source code of a package of an open source software project (Mylyn from Eclipse), and we test them with several other packages and three different small size software projects, using their UML and code metrics for comparison. The results of our empirical study lead us to conclude that the proposed UML RFC and UML CBO metrics can predict fault-proneness of code almost with the same accuracy as their respective code metrics do. The elimination of outliers and the normalization procedure used were of great utility, not only for enabling our UML metrics to predict fault-proneness of code using a code-based prediction model but also for improving the prediction results of our models across different software packages and projects.

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A hybrid faulty module prediction using association rule mining and logistic regression analysis

Cited by 25 publications

References 7 publications

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

A UML Approximation of Three Chidamber-Kemerer Metrics and Their Ability to Predict Faulty Code across Software Projects

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