INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION

2013 ◽  
Vol 23 (02) ◽  
pp. 147-172
Author(s):  
CHING-PAO CHANG
Keyword(s):  
Prediction Model ◽  
Association Rule ◽  
Association Rule Mining ◽  
Software Process ◽  
Negative Association ◽  
Defect Prediction ◽  
Rule Mining ◽  
Mining Technique ◽  
Software Defect ◽  
Recommendations For Action

Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.

Software defect prediction using relational association rule mining

2014 ◽  
Vol 264 ◽  
pp. 260-278 ◽  
Author(s):  
Gabriela Czibula ◽  
Zsuzsanna Marian ◽  
Istvan Gergely Czibula
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Rule Mining ◽  
Software Defect

Software Defect Prediction Based on GUHA Data Mining Procedure and Multi-Objective Pareto Efficient Rule Selection

2014 ◽  
Vol 6 (2) ◽  
pp. 1-29
Author(s):  
Bharavi Mishra ◽  
K.K. Shukla
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Defect Prediction ◽  
Data Sets ◽  
Software Defect Prediction ◽  
Rule Generation ◽  
Rule Mining ◽  
Prediction Rules ◽  
Software Defect ◽  
Pareto Efficient

Software defect prediction, if is effective, enables the developers to distribute their testing efforts efficiently and let them focus on defect prone modules. It would be very resource consuming to test all the modules while the defect lies in fraction of modules. Information about fault-proneness of classes and methods can be used to develop new strategies which can help mitigate the overall development cost and increase the customer satisfaction. Several machine learning strategies have been used in recent past to identify defective modules. These models are built using publicly available historical software defect data sets. Most of the proposed techniques are not able to deal with the class imbalance problem efficiently. Therefore, it is necessary to develop a prediction model which consists of small simple and comprehensible rules. Considering these facts, in this paper, the authors propose a novel defect prediction approach named GUHA based Classification Association Rule Mining algorithm (G-CARM) where “GUHA” stands for General Unary Hypothesis Automaton. G-CARM approach is primarily based on Classification Association Rule Mining, and deploys a two stage process involving attribute discretization, and rule generation using GUHA. GUHA is oldest yet very powerful method of pattern mining. The basic idea of GUHA procedure is to mine the interesting attribute patterns that indicate defect proneness. The new method has been compared against five other models reported in recent literature viz. Naive Bayes, Support Vector Machine, RIPPER, J48 and Nearest Neighbour classifier by using several measures, including AUC and probability of detection. The experimental results indicate that the prediction performance of G-CARM approach is better than other prediction approaches. The authors' approach achieved 76% mean recall and 83% mean precision for defective modules and 93% mean recall and 83% mean precision for non-defective modules on CM1, KC1, KC2 and Eclipse data sets. Further defect rule generation process often generates a large number of rules which require considerable efforts while using these rules as a defect predictor, hence, a rule sub-set selection process is also proposed to select best set of rules according to the requirements. Evolution criteria for defect prediction like sensitivity, specificity, precision often compete against each other. It is therefore, important to use multi-objective optimization algorithms for selecting prediction rules. In this paper the authors report prediction rules that are Pareto efficient in the sense that no further improvements in the rule set is possible without sacrificing some performance criteria. Non-Dominated Sorting Genetic Algorithm has been used to find Pareto front and defect prediction rules.

SOFTWARE DEFECT PREDICTION USING INTERTRANSACTION ASSOCIATION RULE MINING

2009 ◽  
Vol 19 (06) ◽  
pp. 747-764 ◽  
Author(s):  
CHING-PAO CHANG ◽  
CHIH-PING CHU
Keyword(s):  
Software Development ◽  
Prediction Model ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
High Severity ◽  
Software Defect ◽  
Novel Approach ◽  
Defect Prevention ◽  
Significant Effort

Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

scholarly journals Security and privacy concerned association rule mining technique for the accurate frequent pattern identification

2017 ◽  
Vol 7 (1.1) ◽  
pp. 19
Author(s):  
T. Nusrat Jabeen ◽  
M. Chidambaram ◽  
G. Suseendran
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Simulation Environment ◽  
Rule Mining ◽  
Mining Technique ◽  
Diffie Hellman

Security and privacy has emerged to be a serious concern in which the business professional don’t desire to share their classified transaction data. In the earlier work, secured sharing of transaction databases are carried out. The performance of those methods is enhanced further by bringing in Security and Privacy aware Large Database Association Rule Mining (SPLD-ARM) framework. Now the Improved Secured Association Rule Mining (ISARM) is introduced for the horizontal and vertical segmentation of huge database. Then k-Anonymization methods referred to as suppression and generalization based Anonymization method is employed for privacy guarantee. At last, Diffie-Hellman encryption algorithm is presented in order to safeguard the sensitive information and for the storage service provider to work on encrypted information. The Diffie-Hellman algorithm is utilized for increasing the quality of the system on the overall by the generation of the secured keys and thus the actual data is protected more efficiently. Realization of the newly introduced technique is conducted in the java simulation environment that reveals that the newly introduced technique accomplishes privacy in addition to security.

Human Depression Prediction Using Association Rule Mining Technique

2021 ◽  
pp. 223-237
Author(s):  
Md. Al-Mamun Biilah ◽  
M. Raihan ◽  
Tamanna Akter ◽  
Nasif Alvi ◽  
Nusrat Jahan Bristy ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Technique
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