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A Comprehensive Framework for Testing Database-Centric Software Applications

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A Comprehensive Framework for Testing Database-Centric Software Applications A Comprehensive Framework for Testing Database-Centric Software Applications Gregory M. Kapfhammer Department of Computer Science University of Pittsburgh PhD Dissertation Defense April 19, 2007 PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 1 Dissertation Committee Director: Dr. Mary Lou Soffa (University of Virginia) Dr. Panos Chrysanthis (University of Pittsburgh) Dr. Bruce Childers (University of Pittsburgh) Dr. Jeffrey Voas (SAIC) With support and encouragement from countless individuals! PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 2 Motivation The Risks Digest, Volume 22, Issue 64, 2003 Jeppesen reports airspace boundary problems About 350 airspace boundaries contained in Jeppesen NavData are incorrect, the FAA has warned. The error occurred at Jeppe- sen after a software upgrade when information was pulled from a database containing 20,000 airspace boundaries worldwide for the March NavData update, which takes effect March 20. Important Point: Practically all use of databases occurs from within applica- tion programs [Silberschatz et al., 2006, pg. 311]. PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 3 Research Contributions Comprehensive framework that tests a program’s interaction with the complex state and structure of a database Database interaction fault model Database-aware representations Test adequacy Test coverage monitoring Regression testing Worst-case analysis of the algorithms and empirical evaluation with six case study applications PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 4 Traditional Software Testing P Input Output a 5 print ... exit Final Result: 45 Byte Code Virtual Graphical Database Machine Interface File Operating System System Execution Environment Defects (e.g., bugs, faults, errors) can exist in program P and all aspects of P ’s environment PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 5 Testing Environment Interactions P Input Output a 5 print ... exit Final Result: 45 Virtual Machine Database Operating System File System Execution Environment Defects can also exist in P ’s interaction with its environment PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 6 Focus on Database Interactions P m select insert update delete D1 De Program P can view and/or modify the state of the database PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 7 Types of Applications Database-Centric Applications Interaction Approach Program Location Embedded Interface Inside DBMS Outside DBMS Testing framework relevant to all types of applications Current tool support focuses on Interface-Outside applications Example: Java application that submits SQL Strings to HSQLDB relational database using JDBC drivers PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 8 Research Contributions Database Interaction Fault Model Test Adequacy Criteria Test Coverage Monitoring Regression Testing Reduction Prioritization PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 9 Database Interaction Faults: (1-v) P uses update or P m insert to incorrectly modify items within database insert update Commission fault that violates expected database validity Database-aware before after adequacy criteria actual can support fault isolation PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 10 Database Interaction Faults: (1-c) m P uses delete to P remove incorrect items from database delete Commission fault that violates database expected completeness Database-aware before after adequacy criteria can actual support fault isolation PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 11 Data Flow-Based Test Adequacy P 3 delete from R define(R) where A > 100 m i 6 select * from R use(R) The intraprocedural database interaction association hn3; n6; Ri exists within method mi PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 12 Research Contributions Database Interaction Fault Model Test Adequacy Criteria Test Coverage Monitoring Regression Testing Reduction Prioritization PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 13 Test Adequacy Component P, C Representation ICFG Constructor Database-Aware Database Representation Constructor Database Database Interaction Entities Database Interaction ICFG Data Flow Analyzer (DI-ICFG) Analyzer Database Interaction Associations Process: Create a database-aware representation and perform data flow analysis Purpose: Identify the database interaction associations (i.e., the test requirements) PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 14 Database-Aware Representation Database interaction qu_lck = "UPDATE UserInfo ..." + temp1 + ";" graphs (DIGs) are update_lock = m_connect.createStatement() placed before interaction entry G entry Gr 1 A r2 D point define(temp3) define(temp2) Multiple DIGs can be use(temp4) exit Gr1 integrated into a single exit G r 2 CFG Ir result_lock = update_lock.executeUpdate(qu_lck) Analyze interaction in a if( result_lock == 1) control-flow sensitive completed = true fashion exit lockAccount PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 15 Data Flow Time Overhead P P+D P+R P+Rc P+A P+Av 25 20.50 20.74 20.77 20.78 20.94 21.06 20 L 15 sec H 10 Time 5 P P+D P+R P+Rc P+A P+Av Database Granularity 2:7% increase in time overhead from P to P + Av (TM) PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 16 Research Contributions Database Interaction Fault Model Test Adequacy Criteria Test Coverage Monitoring Regression Testing Reduction Prioritization PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 17 Database-Aware Coverage Monitoring Program Test Suite Adequacy Criteria Instrumented Program Instrumentation Instrumented Test Suite Test Suite Coverage Execution Results Test Adequacy Requirements Calculation Adequacy Measurements Purpose: Record how the program interacts with the database during test suite execution PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 18 Database-Aware Instrumentation Test Coverage Monitoring Instrumentation Interaction Location Interaction Type Program Test Suite Defining Using Defining-Using Efficiently monitor coverage without changing the behavior of the program under test Record coverage information in a database interaction calling context tree (DI-CCT) PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 19 Configuring the Coverage Monitor Configuration of the Test Coverage Monitor Instrumentation Tree Format Tree Type Tree Storage Static Dynamic Binary XML Traditional Database-Aware Standard Compressed Source Code Bytecode CCT DCT Interaction Level DI-DCT DI-CCT Database Relation Attribute Record Attribute Value Flexible and efficient approach that fully supports both traditional and database-centric applications PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 20 Static Instrumentation: Time FF PI RM ST TM GB All 10 L 8.687 sec H 8 Time 6 5.583 5.169 4.391 4.404 4.396 4.394 4 Instrumentation 2 Static FF PI RM ST TM GB All Application Attach probes to all of the applications in less than nine seconds Static approach is less flexible than dynamic instrumentation PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 21 Static Instrumentation: Space A As ZIP GZIP PACK 80000 75782 L 68456 68475 bytes H 60000 Size 40000 25084 19419 20000 Application 9034 ZIP GZIP PACK Compression Technique GB Increase in bytecode size may be largeH L (space vs. time trade-off) PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 22 Static vs. Dynamic Instrumentation Norm Sta -CCT Sta -DCT Dyn -CCT Dyn -DCT 14 11.435 12 11.084 10 L 8.026 7.626 sec 8 H 6.939 Time 6 TCM 4 2 Norm Sta -CCT Sta -DCT Dyn -CCT Dyn -DCT Instrumentation Technique - Tree Type GB Static is faster than dynamic / CCT is HfasterL than DCT The coverage monitor is both efficient and effective PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 23 Size of the Instrumented Applications Compr Tech Before Instr (bytes) After Instr (bytes) None 29275 887609 Zip 15623 41351 Gzip 10624 35594 Pack 5699 34497 Average static size across all case study applications Compress the bytecodes with general purpose techniques Specialized compressor nicely reduces space overhead PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 24 Database Interaction Levels CCT Interaction Level TCM Time (sec) Percent Increase (%) Program 7.44 12.39 Database 7.51 13.44 Relation 7.56 14.20 Attribute 8.91 34.59 Record 8.90 34.44 Attribute Value 10.14 53.17 Static instrumentation supports efficient monitoring 53% increase in testing time at finest level of interaction PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 25 Research Contributions Database Interaction Fault Model Test Adequacy Criteria Test Coverage Monitoring Regression Testing Reduction Prioritization PhD Dissertation Defense, University of Pittsburgh, April 19, 2007 – p. 26 Database-Aware Regression Testing Original Test Suite Program Reduction Modified Test Suite Begin Coverage Report Testing Results End or Prioritization Test Suite Execution Program or Database Changes Version specific vs. general approach Use paths in the coverage tree as a test requirement Prioritization re-orders the test suite so that it is more effective Reduction
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