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An Empirical Study of Execution-Data Classification Based on Machine Learning Dan Hao, Xingxia Wu, Lu Zhang Key Laboratory of High Confidence Software Technologies, Ministry of Education <strong>Institute</strong> of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, P. R. China {haod, wuxx10, zhanglu}@sei.pku.edu.cn Abstract As it may be difficult for users to distinguish a passing execution from a failing execution for a released software system, researchers have proposed to apply the Random Forest algorithm to classify remotely-collected program execution data. In general, execution-data classification can be viewed as a machine-learning problem, in which a trained learner needs to classify whether each execution is a passing execution or a failing execution. In this paper, we report an empirical study that further investigates various issues in execution-data classification based on machine learning. Compared with previous research, our study further investigates the impact of the following issues: different machinelearning algorithms, the numbers of training instances to construct a classification model, and different types of execution data. 1. Introduction After a software system is delivered to its different users, it may be still necessary to analyze or measure the released software system to improve software quality. Although a software system usually has been tested before delivery, the testing process cannot guarantee the reliability of the release. Before delivering a software system, developers need to test the software system in-house, assuming that the software system will be used in the field as it is tested. However, this assumption may not always be satisfied [9], due to varied running environments or unexpected inputs for instance. Consequently, a software system still needs to be tested and analyzed after it is delivered. However, as a released software system runs on sites of remote users, the testing and analysis activities on a released software system are different from those in-house. For instance, developers can hardly detect failures directly based on the behavior of a released software system because its behavior occurs on the sites of remote users. Moreover, analysis on a released software system may require instrumentation, and only lightweight instrumentation is allowed so as to incur as little as possible extra running cost. Consequently, several techniques [6, 7, 11] are proposed to support Remote execution Analysis and Measurement of Software <strong>Systems</strong> (abbreviated as RAMSS by Haran and colleagues [5]). RAMSS techniques usually collect data by instrumenting instances of a software system used by different remote users and then analyze the collected execution data. In RAMSS, a fundamental issue is to distinguish a failing execution from a passing execution. Addressing this issue helps developers understand the behavior of a released software system. For instance, developers know how often failure occurs in the field when remote users run the released software system by counting the numbers of passing executions and failing executions. Moreover, distinguishing a failing execution from a passing execution is also important for developers to debug a released software system because such information helps developers know in what circumstances failures occur. To automate distinguishing failing executions from passing ones, Haran and colleagues [5] proposed to collect execution data in the field and distinguish a failing execution from a passing execution by applying a machine-learning algorithm (i.e., the Random Forest algorithm) to the collected execution data. According to the empirical study conducted by Haran and colleagues [5], their approach is promising, as it can reliably and efficiently classify the execution data. However, there are several important factors not addressed in their study. First, their study investigated only one machine-learning algorithm, but there are quite afewmachine-learning algorithms for classification. Second, their study did not investigate the impact of the number of instances for constructing the classification model, but this number may be an important factor of the overhead 283
of execution-data classification. Third, their study investigated execution data only in the form of counts for methods, statements and so on, but did not investigate execution data in the form of coverage, which is widely used in practice. In this paper, we report an empirical study that evaluates the impact of these three factors on the effectiveness of execution-data classification. The aim of our empirical study is to provide evidence on the determination of the three important factors in practice. For the first factor, we investigate three popular machine-learning algorithms: the Random Forest algorithm (RF), the Sequential Minimal Optimization algorithm(SMO), and the Naive Bayes algorithm(NB). For the second factor, we investigate training sets of different sizes. For the third factor, we investigate five types of execution data: the statement count, the statement coverage, the method count, the method coverage, and the branch coverage. The main contributions of this paper are as follows. First, to our knowledge, our study is the first empirical study on the impact of the three factors in execution-data classification based on machine learning. Second, our study provides guidelines for the application of execution-data classification based on machine learning regarding the choice of machine-learning algorithms, the number of training instances, and the type of execution data. 2. Execution-Data Classification Based on Machine Learning Haran and colleagues [5] proposed to collect execution data by instrumenting instances of a released software system and then classify passing executions and failing executions based on these collected execution data using the Random Forest algorithm, which is an implementation of treebased classification. This technique can be viewed as an application of a classification algorithm in machine learning for execution-data classification. In the following, we generalize Haran and colleagues’ technique to execution-data classification based on machine learning. For the ease of presentation, let us consider the execution data based on the statement count. For a program consisting of n statements (denoted as ST={st 1 ,st 2 , ..., st n }), the execution data of a test case (denoted as t) is represented as a series of numbers, each of which is the number of times a statement is executed when using t to test the program. All the test cases (whose execution data are already obtained) are divided into two sets: the training set and the testing set 1 . Each test case in the training set is also associated with a label that indicates whether the test case is a passing one or a failing one. For test cases in the testing set, it is unknown whether each such test case is a passing one or a failing one. The problem of execution-data classification is 1 The training set and the testing set are two terminologies in machine learning. Note that the testing set is not related to software testing. Table 1. Subjects Program Ver. Test TrainingSet LOC(Exe) Met. Bra. print tokens 7 4,072 41∼203 565(203) 18 35 print tokens2 10 4,057 41∼203 510(203) 18 70 replace 32 5,542 58∼289 563(289) 21 61 schedule 9 2,627 33∼162 412(162) 18 25 schedule2 10 2,683 29∼144 307(144) 16 31 tcas 41 1,592 14∼67 173(67) 9 16 tot info 23 1,026 14∼136 406(136) 7 36 Space 38 13,585 1,244∼6,218 9,564(6218) 136 530 to use the training set to predict whether each test case in the testing set is a passing one or a failing one. 3. Experimental Study In this experiment, we plan to evaluate the impact of three factors in execution-data classification so as to answer the following three research questions. RQ1— which machine-learning algorithm produces the best results in execution-data classification? RQ2— what is the minimal number of training instances that are needed to produce good enough results in execution-data classification? RQ3— which type of execution-data produces the best results in execution-data classification? Table 1 presents the details of the eight C subjects used in this experiment, whose source code and test collections are available from Software-artifact Infrastructure Repository (http://sir.unl.edu/portal/index.php) [3]. Specifically, the table gives the name of each subject, number of faulty versions, number of test cases in the test collections, the range of the numbers of training instances used to construct a classification model in our empirical study, the number of lines of code, and the number of branch statements. Moreover, the “Exe” (abbreviation of executable statements) within parentheses represents the number of executable statements, which presents the real size of a subject. As the faulty programs in Table 1 are all single-fault programs, we totally constructed 80 multiple-fault programs for the eight subjects by extracting the faults from the single-fault programs of each subject and then seeding more than one faults into each subject. 3.1. Independent and Dependent Variables The independent variables come from the three research questions and are defined as follows. The first independent variable Algorithm refers to the machine-learning algorithm that is used to build a classification model. Many algorithms have been proposed in the literature of machine learning. Here we investigate the following three representative algorithms: Random Forest (RF) [2], Naive Bayes (NB) [8], and Sequential Minimal Optimization (SMO) [12], because these algorithms have been found to be effective in the literature of machine learning and have been widely used. Moreover, these algorithms 284
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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II. RELATED WORK Business processes
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test, we did not find any significa
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the model checkers SPIN [8] and NuS
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Data Set TABLE II. DATA SETS Artifa
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Progressive Clustering with Learned
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Multi-Objective Optimization of Fuz
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Aspect-Orientation in the Developme
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Evaluating Open Source Reverse Engi
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Improving Program Comprehension in
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properties. In particular, the foll
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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An Exploratory Study of One-Use and
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subject also caused outliers for re
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Choosing licenses in free open sour
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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Gupta extended HGS and proposed the
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satisfies R( D) R( C) S 1. Compa
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sequence. Each t i θ i in the firi
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High-level Petri net Place Place Ty
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access control exists, the action i
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Connectors for Secure Software Arch
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Computer Forensics: Toward the Cons
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A Holistic Approach to Software Tra
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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which the airport data did not cont
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classified. Recall is the probabili
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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way to detect something which could
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obtain the input of experts on desi
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DC2AP Element and their Application
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2.3.1 Complementarity of MDD techno
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ecause the system of C-net model is
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Identification Guidelines for the D
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created according to the preview re
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In the following, we present the re
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USE SCENARIO The application Vuelos
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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comparison rule, a value of 1/9 is
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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A Mapping Study on Software Product
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most relevant sources in software e
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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A Variability Management Method for
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
Page 729 and 730:
A Process-Based Approach to Improvi
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Appropriate processes m ust support
Page 733 and 734:
Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
Page 737 and 738:
III. VALIDATING THE DIMENSION HEADE
Page 739 and 740:
The table title and the dimension s
Page 741 and 742:
A light weight alternative for OLAP
Page 743 and 744:
i.d. subsets of cubes focused on se
Page 745 and 746:
Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
Page 751 and 752:
shown at Figure 5. Objectives are s
Page 753 and 754:
Rendering UML Activity Diagrams as
Page 755 and 756:
a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
Page 759 and 760:
umlTUowl - A Both Generic and Vendo
Page 761 and 762:
The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
Page 767 and 768:
III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
Page 773 and 774:
744
Page 775 and 776:
746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
756
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758
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With the GDUC approach, we can mode
Page 791 and 792:
Figure 6. Three proposals containin
Page 793 and 794:
764
Page 795 and 796:
766
Page 797 and 798:
Proactive Two Way Mobile Advertisem
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
Page 811 and 812:
Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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