Handbook of software fault localization : foundations and advances / / edited by W. Eric Wong, T. H. Tse

• Record Nr.: UNINA9910830449103321
• Pubbl/distr/stampa: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]; ©2023
• ISBN: 1-119-88092-0; 1-119-29181-X
• Descrizione fisica: 1 online resource (611 pages)
• Disciplina: 005.1
• Soggetti: Software failures; Software failures - Data processing; Debugging in computer science; Computer software - Quality control
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Editor Biographies -- List of Contributors -- Chapter 1 Software Fault Localization: an Overview of Research, Techniques, and Tools -- 1.1 Introduction -- 1.2 Traditional Fault Localization Techniques -- 1.2.1 Program Logging -- 1.2.2 Assertions -- 1.2.3 Breakpoints -- 1.2.4 Profiling -- 1.3 Advanced Fault Localization Techniques -- 1.3.1 Slicing-Based Techniques -- 1.3.2 Program Spectrum-Based Techniques -- 1.3.2.1 Notation -- 1.4 Subject Programs -- 1.5 Evaluation Metrics -- 1.6 Software Fault Localization Tools -- 1.7 Critical Aspects -- 1.7.1 Fault Localization with Multiple Bugs -- 1.7.2 Inputs, Outputs, and Impact of Test Cases -- 1.7.3 Coincidental Correctness -- 1.7.4 Faults Introduced by Missing Code -- 1.7.5 Combination of Multiple Fault Localization Techniques -- 1.7.6 Ties Within Fault Localization Rankings -- 1.7.7 Fault Localization for Concurrency Bugs -- 1.7.8 Spreadsheet Fault Localization -- 1.7.9 Theoretical Studies -- 1.8 Conclusion -- 1.8 Notes -- References -- Chapter 2 Traditional Techniques for Software Fault Localization -- 2.1 Program Logging -- 2.2 Assertions -- 2.3 Breakpoints -- 2.4 Profiling ; -- 2.5 Discussion -- 2.6 Conclusion -- References -- Chapter 3 Slicing-Based Techniques for Software Fault Localization -- 3.1 Introduction -- 3.2 Static Slicing-Based Fault Localization -- 3.2.1 Introduction -- 3.2.2 Program Slicing Combined with Equivalence Analysis -- 3.2.3 Further Application -- 3.3 Dynamic Slicing-Based Fault Localization -- 3.3.1 Dynamic Slicing and Backtracking Techniques -- 3.3.2 Dynamic Slicing and Model-Based Techniques -- 3.3.3 Critical Slicing -- 3.3.3.1 Relationships Between Critical Slices (CS) and Exact Dynamic Program Slices (DPS) -- 3.3.3.2 Relationship Between Critical Slices and Executed Static Program Slices -- 3.3.3.3 Construction Cost.; 3.3.4 Multiple-Points Dynamic Slicing -- 3.3.4.1 BwS of an Erroneous Computed Value -- 3.3.4.2 FwS of Failure-Inducing Input Difference -- 3.3.4.3 BiS of a Critical Predicate -- 3.3.4.4 MPSs: Dynamic Chops -- 3.3.5 Execution Indexing -- 3.3.5.1 Concepts -- 3.3.5.2 Structural Indexing -- 3.3.6 Dual Slicing to Locate Concurrency Bugs -- 3.3.6.1 Trace Comparison -- 3.3.6.2 Dual Slicing -- 3.3.7 Comparative Causality: a Causal Inference Model Based on Dual Slicing -- 3.3.7.1 Property One: Relevance -- 3.3.7.2 Property Two: Sufficiency -- 3.3.8 Implicit Dependences to Locate Execution Omission Errors -- 3.3.9 Other Dynamic Slicing-Based Techniques -- 3.4 Execution Slicing-Based Fault Localization -- 3.4.1 Fault Localization Using Execution Dice -- 3.4.2 A Family of Fault Localization Heuristics Based on Execution Slicing -- 3.4.2.1 Heuristic I -- 3.4.2.2 Heuristic II -- 3.4.2.3 Heuristic III -- 3.4.3 Effective Fault Localization Based on Execution Slices and Inter-block Data Dependence -- 3.4.3.1 Augmenting a Bad D(1) -- 3.4.3.2 Refining a Good D(1) -- 3.4.3.3 An Incremental Debugging Strategy -- 3.4.4 Other Execution Slicing-Based Techniques in Software Fault Localization -- 3.5 Discussions -- 3.6 Conclusion -- 3.6 Notes -- References -- Chapter 4 Spectrum-Based Techniques for Software Fault Localization -- 4.1 Introduction -- 4.2 Background and Notation -- 4.2.1 Similarity Coefficient-Based Fault Localization -- 4.2.2 An Example of Using Similarity Coefficient to Compute Suspiciousness -- 4.3 Insights of Some Spectra-Based Metrics -- 4.4 Equivalence Metrics -- 4.4.1 Applicability of the Equivalence Relation to Other Fault Localization Techniques -- 4.4.2 Applicability Beyond Fault Localization -- 4.5 Selecting a Good Suspiciousness Function (Metric) -- 4.5.1 Cost of Using a Metric -- 4.5.2 Optimality for Programs with a Single Bug.; 4.5.3 Optimality for Programs with Deterministic Bugs -- 4.6 Using Spectrum-Based Metrics for Fault Localization -- 4.6.1 Spectrum-Based Metrics for Fault Localization -- 4.6.2 Refinement of Spectra-Based Metrics -- 4.7 Empirical Evaluation Studies of SBFL Metrics -- 4.7.1 The Construction of D* -- 4.7.2 An Illustrative Example -- 4.7.3 A Case Study Using D* -- 4.7.3.1 Subject Programs -- 4.7.3.2 Fault Localization Techniques Used in Comparisons -- 4.7.3.3 Evaluation Metrics and Criteria -- 4.7.3.3.1 The EXAM Score -- 4.7.3.3.2 Cumulative Number of Statements Examined -- 4.7.3.3.3 Wilcoxon Signed-Rank Test -- 4.7.3.4 Statement with Same Suspiciousness Values -- 4.7.3.5 Results -- 4.7.3.6 Effectiveness of D* with Different Values of * -- 4.7.3.7 D* Versus Other Fault Localization Techniques -- 4.7.3.8 Programs with Multiple Bugs -- 4.7.3.8.1 The Expense Score-Based Approach -- 4.7.3.8.2 The One-Bug-at-a-Time Approach -- 4.7.3.9 Discussion -- 4.7.3.9.1 Intuition -- 4.7.3.9.2 Best and Worst Effectiveness Versus Other Alternatives -- 4.7.3.9.3 Threats to Validity -- 4.8 Conclusion -- 4.8 Notes -- References -- Chapter 5 Statistics-Based Techniques for Software Fault Localization -- 5.1 Introduction -- 5.1.1 Tarantula -- 5.1.2 How It Works -- 5.2 Working with Statements ; -- 5.2.1 Techniques Under the Same Problem Settings -- 5.2.2 Statistical Variances -- 5.3 Working with Non-statements -- 5.3.1 Predicate: a Popular Trend -- 5.3.2 BPEL: a Sample Application -- 5.4 Purifying the Input -- 5.4.1 Coincidental Correctness Issue -- 5.4.2 Class Balance Consideration -- 5.5 Reinterpreting the Output -- 5.5.1 Revealing Fault Number -- 5.5.2 Noise Reduction -- 5.5.2 Notes -- References -- Chapter 6 Machine Learning-Based Techniques for Software Fault Localization -- 6.1 Introduction -- 6.2 BP Neural Network-Based Fault Localization.; 6.2.1 Fault Localization with a BP Neural Network -- 6.2.2 Reduce the Number of Candidate Suspicious Statements -- 6.3 RBF Neural Network-Based Fault Localization -- 6.3.1 RBF Neural Networks -- 6.3.2 Methodology -- 6.3.2.1 Fault Localization Using an RBF Neural Network -- 6.3.2.2 Training of the RBF Neural Network -- 6.3.2.3 Definition of a Weighted Bit-Comparison-Based Dissimilarity -- 6.4 C4.5 Decision Tree-Based Fault Localization -- 6.4.1 Category-Partition for Rule Induction -- 6.4.2 Rule Induction Algorithms -- 6.4.3 Statement Ranking Strategies -- 6.4.3.1 Revisiting Tarantula -- 6.4.3.2 Ranking Statements Based on C4.5 Rules -- 6.4.3.2.1 Heuristic -- 6.4.3.2.2 Computing a Statement Ranking -- 6.4.3.2.3 Rule Selection -- 6.5 Applying Simulated Annealing with Statement Pruning for an SBFL Formula -- 6.6 Conclusion -- 6.6 Notes -- References -- Chapter 7 Data Mining-Based Techniques for Software Fault Localization -- 7.1 Introduction -- 7.2 Formal Concept Analysis and Association Rules -- 7.2.1 Formal Concept Analysis -- 7.2.2 Association Rules -- 7.3 Data Mining for Fault Localization -- 7.3.1 Failure Rules -- 7.3.2 Failure Lattice -- 7.4 The Failure Lattice for Multiple Faults -- 7.4.1 Dependencies Between Faults -- 7.4.2 Example -- 7.5 Discussion -- 7.5.1 The Structure of the Execution Traces -- 7.5.2 Union Model -- 7.5.3 Inter section Model -- 7.5.4 Nearest Neighbor -- 7.5.5 Delta Debugging -- 7.5.6 From the Trace Context to the Failure Context -- 7.5.7 The Structure of Association Rules -- 7.5.8 Multiple Faults -- 7.6 Fault Localization Using N-gram Analysis -- 7.6.1 Background -- 7.6.1.1 Execution Sequence -- 7.6.1.2 N-gram. Analysis -- 7.6.1.3 Linear Execution Blocks -- 7.6.1.4 Association Rule Mining -- 7.6.2 Methodology -- 7.6.3 Conclusion -- 7.7 Fault Localization for GUI Software Using N-gram Analysis -- 7.7.1 Background.; 7.7.1.1 Representation of the GUI and Its Operations -- 7.7.1.2 Event Handler -- 7.7.1.3 N-gram -- 7.7.2 Association Rule Mining -- 7.7.3 Methodology -- 7.7.3.1 General Approach -- 7.7.3.2 N-gram Fault Localization Algorithm -- 7.8 Conclusion -- 7.8 Notes -- References -- Chapter 8 Information Retrieval-Based Techniques for Software Fault Localization -- 8.1 Introduction -- 8.2 General IR-Based Fault Localization Process -- 8.3 Fundamental Information Retrieval Techniques for Software Fault Localization -- 8.3.1 Vector Space Model -- 8.3.2 Topic Modeling -- 8.3.3 Word Embedding -- 8.4 Evaluation Metrics -- 8.4.1 Top-k Prediction Accuracy -- 8.4.2 Mean Reciprocal Rank (MRR) -- 8.4.3 Mean Average Precision (MAP) -- 8.5 Techniques for Different Scenarios -- 8.5.1 Text of Current Bug Report Only -- 8.5.1.1 VSM Variants -- 8.5.1.2 Topic Modeling -- 8.5.2 Text and History -- 8.5.2.1 VSM Variants -- 8.5.2.2 Topic Modeling -- 8.5.2.3 Deep Learning -- 8.5.3 Text and Stack/Execution Traces -- 8.6 Empirical Studies -- 8.7 Miscellaneous -- 8.8 Conclusion -- 8.8 Notes -- References -- Chapter 9 Model-Based Techniques for Software Fault Localization -- 9.1 Introduction -- 9.2 Basic Definitions and Algorithms -- 9.2.1 Algorithms for MBD -- 9.3 Modeling for MBD -- 9.3.1 The Value-Based Model -- 9.3.2 The Dependency-Based Model -- 9.3.3 Approximation Models for Debugging -- 9.3.4 Other Modeling ; Approaches -- 9.4 Application Areas -- 9.5 Hybrid Approaches -- 9.6 Conclusion -- 9.6 Notes -- References -- Chapter 10 Software Fault Localization in Spreadsheets -- 10.1 Motivation -- 10.2 Definition of the Spreadsheet Language -- 10.3 Cones -- 10.4 Spectrum-Based Fault Localization -- 10.5 Model-Based Spreadsheet Debugging -- 10.6 Repair Approaches -- 10.7 Checking Approaches -- 10.8 Testing -- 10.9 Conclusion -- 10.9 Notes -- References.; Chapter 11 Theoretical Aspects of Software Fault Localization.