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.

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.

scholarly journals Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care

2021 ◽  
Vol 8 ◽  
Author(s):  
Oliver Haas ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Risk Assessment ◽  
Critical Care ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Mortality Risk ◽  
Association Rule ◽  
Minimal Model ◽  
Association Rule Mining ◽  
Full Model ◽  
Rule Mining

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

scholarly journals Identification of Defect Generation Rules among Defects in Construction Projects Using Association Rule Mining

2020 ◽  
Vol 12 (9) ◽  
pp. 3875
Author(s):  
Jungeun Park ◽  
Yongwoon Cha ◽  
Hamad Al Jassmi ◽  
Sangwon Han ◽  
Chang-taek Hyun
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Construction Projects ◽  
Mitigation Strategies ◽  
Construction Work ◽  
Construction Sites ◽  
Rule Mining ◽  
Work Process ◽  
Defect Generation ◽  
Defect Prevention

This study aims to identify the defect generation rules between defects, to support effective defect prevention at construction sites. Numerous studies have been performed to identify the relations between defect causes, to prevent defects in construction projects. However, identifying the inter-causal pattern does not yet guarantee an ultimate grasp of what constitutes proper defect mitigation strategies, unless the underlying defect-to-defect generation rules are thoroughly understood too. Specifically, if a defect generated in a work process is ignored without taking necessary corrective action, then additional defects could be generated in its following works as well. Thus, to minimize defect generation, this study analyzes the defects in the sequence of a construction work. To achieve this, the authors collected 9054 defect data, and association rule mining is used to analyze the rules between the defects. Consequently, 216 rules are identified, and 152 rules are classified into 3 categories along with 4 experts (71 expected rules, 22 unexpected but explainable rules, and 59 unexpected and unexplainable rules). The generation rules between the defects identified in this study are expected to be used to regularize various defect types to determine those that require priority management.

Prediction of the Next Question for the Question Answering System

2016 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Manvi Breja
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Question Answering ◽  
User Profile ◽  
User Profiling ◽  
Rule Mining ◽  
Question Answering System ◽  
Novel Approach ◽  
Initial Question

<span>User profiling, one of the main issue faced while implementing the efficient question answering system, in which the user profile is made, containing the data posed by the user, capturing their domain of interest. The paper presents the method of predicting the next related questions to the first initial question provided by the user to the question answering search engine. A novel approach of the association rule mining is highlighted in which the information is extracted from the log of the previously submitted questions to the question answering search engine, using algorithms for mining association rules and predicts the set of next questions that the user will provide to the system in the next session. Using this approach, the question answering system keeps the relevant answers of the next questions in the repository for providing a speedy response to the user and thus increasing the efficiency of the system.</span>

A novel approach to adaptive relational association rule mining

2015 ◽  
Vol 36 ◽  
pp. 519-533 ◽  
Author(s):  
Gabriela Czibula ◽  
Istvan Gergely Czibula ◽  
Adela-Maria Sîrbu ◽  
Ioan-Gabriel Mircea
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Novel Approach

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

2019 ◽  
Vol 18 (03) ◽  
pp. 1950028
Author(s):  
Sheel Shalini ◽  
Kanhaiya Lal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Search Space ◽  
Temporal Association ◽  
Incremental Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Temporal Association Rule ◽  
Mining Algorithms

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

Effective Mining of Weighted Fuzzy Association Rules

2010 ◽  
pp. 47-64 ◽  
Author(s):  
Maybin Muyeba ◽  
M. Sulaiman Khan ◽  
Frans Coenen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Data ◽  
Closure Property ◽  
Data Sets ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Novel Approach ◽  
Downward Closure

A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.

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