Holdings: Sharing data and models in software engineering

Sharing data and models in software engineering:

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Bibliographic Details
Format:	Book
Language:	English
Published:	Amsterdam [u.a.] Morgan Kaufmann/Elsevier 2015
Subjects:	Software Engineering Verteiltes System Modellierung
Online Access:	Inhaltsverzeichnis
Physical Description:	XXVII, 378 S. Illustrationen, Diagramme
ISBN:	9780124172951

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adam_text	Contents í » »ι Ы heures ........................................................................................... xix CHAPTER 1 Introduction ........................................................................ ι 1.1 Why Read This Book? ............................................................... 1 1.2 What Do We Mean by Sharing ? ................................................... 1 1.2.1 Sharing Insights .............................................................. 2 1.2.2 Sharing Models .............................................................. 2 1.2.3 Sharing Data ................................................................. 3 1 .2.4 Sharing Analysis Methods .................................................. 3 1 .2.5 Types of Sharing ............................................................. 3 1 .2.6 Challenges with Sharing .................................................... 4 1.2.7 How to Share ................................................................. 5 1.3 What? (Our Executive Summary) .................................................... 7 1.3. 1 An Overview ................................................................. 7 1.3.2 More Details ................................................................. Η 1.4 How to Read This Book .............................................................. 9 1 .4. 1 Data Analysis Patterns ...................................................... 10 1.5 Bui What About ...? (What Is Not in This Book) .................................. 10 1.5.1 What About Big Data ? ................................................... 10 1.5.2 What About Related Work? ................................................. 11 1.5.3 Why All the Defect Prediction and Effort Estimation? .................................................................. II 1.6 Who? (About the Authors) ........................................................... 12 1.7 Who Else? (Acknowledgments) ..................................................... 13 PARTI DATA MINING FOR MANAGERS _____________________________ CHAPTER 2 Rules for Managers .............................................................. 17 2.1 The Inductive Engineering Manifesto ............................................... 17 22 More Rules ............................................................................ 18 CHAPTERS Rule #1: Talk to the Users ..................................................... 19 3.1 UscrsBiases .......................................................................... 19 32 Data Mining Biases ................................................................... 20 3.3 Can We Avoid Bias? .................................................................. 22 3.4 Managing Biases ...................................................................... 22 3.5 Summary .............................................................................. 23 ix CONTENTS CHAPTER 4 Rule #2: Know the Domain 2S 4.1 Cautionary Talc # I : - Discovering Random Noise ................................. њ 4.2 Cautionary Tale #2: Jumping al Shadows ........................................... 27 4.3 Cautionary Talc #3: It Pays to Ask .................................................. 27 4.4 Summary .............................................................................. CHAPTER 5 Rule #3: Suspect Your Oata 29 5.1 Controlling Data Collection .......................................................... 29 5.2 Problems with Controlled Data Collection .......................................... 29 5.3 Rinse (and Prune) Before Use ....................................................... 30 5.3.1 Row Pruning ................................................................. 30 5.3.2 Column Pruning ............................................................. 30 5.4 On the Value of Pruning .............................................................. 31 5.5 Summary .............................................................................. 34 CHAPTER 6 Rule #4: Data Science Is Cyclic ............................................. 35 6.1 The Knowledge Discovery Cycle .................................................... 35 6.2 Evolving Cyclic Development ....................................................... 37 6.2.1 Scouting ...................................................................... 37 6.2.2 Surveying .................................................................... 38 6.2.3 Building ...................................................................... 38 6.2.4 Effort ......................................................................... 38 6.3 Summary .............................................................................. 38 PART И DATA MINING; A TECHNICAL TUTORIAL _____________________ CHAPTER 7 Oata Mining and SE .............................................................. 4i 7.1 Some Definitions ..................................................................... 41 7»2 Some Application Areas ............................................................. 41 CHAPTER 8 Defect Prediction ................................................................. 43 Є.1 Defect Detection Economics ...................................................... 43 82 Static Code Defect Prediction ........................................................ 45 8.2. 1 Easy Co Use ................................................ 45 8.2.2 Widely Used ...................................... і!!. . !. . . . . !. !. ......... 45 8.2.3 Useful .......................................... i . ..!! !.....!..!......! 45 CHAPTERS Effort Estimation .................................................................. 47 8.1 The Estimation Problem ..................................... 47 Ä2 How 10 Make Estimates .......................................... 4g 9.2.1 Expert-Based Estimation ..................................... 4g 9.2.2 Model-Based Estimation .................................. 49 9.23 Hybrid Methods ........................... 50 CONTENTS xi CHAPTER 10 Data Mining (Under the Hood) ................................................ si 10.1 Data Carving .......................................................................... 51 10.2 About lhe Data ........................................................................ 52 10.3 Cohen Pruning ........................................................................ 53 10.4 Discretization ......................................................................... 55 10.4.1 ( )ther Discretization Methods ............................................... 55 10.5 Column Pruning ...................................................................... 56 10.6 Row Pruning .......................................................................... 57 10.7 Cluster Pruning ....................................................................... 58 10.7.1 Advantages of Prototypes ................................................... 60 1 0.7.2 Advantages of Clustering ................................................... 61 10.8 Contrast Pruning ...................................................................... 62 10.9 Goal Pruning .......................................................................... 64 10.10 Extensions for Continuous Classes .................................................. 67 10.10.1 How RTs Work .............................................................. 67 10. 1 0.2 Creating Splits for Categorical Input Features ............................. 68 10.10.3 Splits on Numeric Input Features ........................................... 71 10. 1 0.4 Termination Condition and Predictions ..................................... 74 10.10.5 Potential Advantages of RTs for Software Effort Estimation ............. 74 10.10.6 Predictions for Multiple Numeric Goals .................................... 75 PARTIU SHARING DATA __________________________________________ CHAPTER 11 Sharing Data: Challenges and Methods .................................... 79 11.1 Houston, We Have a Problem ........................................................ 79 11.2 (кнкі News, Everyone ................................................................ 80 CHAPTER 12 Learning Contexts ................................................................ 83 12.1 Background ........................................................................... 84 12.2 Manual Methods for Contextualization .............................................. 84 12.3 Automatic Methods ................................................................... 87 12.4 ( )ther Motivation to lind Contexts .................................................. 88 12.4.1 Variance Reduction .......................................................... 88 1 2.4.2 Anomaly Detection .......................................................... 88 1 2.4.3 Certification Envelopes ...................................................... 89 1 2.4.4 Incremental learning ........................................................ 89 1 2.4.5 Compression ................................................................. 89 1 2.4.6 ( )ptimi/ation ................................................................. 89 12.5 How to Find Local Regions .......................................................... 90 12.5.1 License ....................................................................... 90 12.5.2 Installing CHUNK ........................................................... 90 xü CONTENTS 12.5.3 Testing Your Installation .................................................... 90 12.5.4 Applying CHUNK to Other Models ........................................ 92 12.6 insideCHUNK ....................................................................... 93 12.6.1 Roadmap to Functions ....................................................... ^ 12.6.2 Distance Calculations ....................................................... 93 12.6.3 Dividing the Data ............................................................ 94 12.6.4 Support Utilities ............................................................. 96 12.7 Putting It all Together ................................................................ 98 12.7.1 _nasa93 ....................................................................... 98 12.8 Using CHUNK ........................................................................ 12.9 Closing Remarks ...................................................................... 10° CHAPTER 13 Cross-Company Learning: Handling the Data Drought ιοί 13.1 Motivation ............................................................................. 1°2 13.2 Setting the Ground for Analyses ..................................................... 103 13.2.1 Wail... Is This Really CC Data? ...........................................105 13.2.2 Mining the Data .............................................................. 105 13.2.3 Magic Trick: NN Relevancy Filtering ......................................106 13.3 Analysis #1 : Can CC Data be Useful for an Organization? ........................107 13.3.1 Design ........................................................................ 107 13.3.2 Results from Analysis #1.................................................... 108 13.3.3 Checking the Analysis #1 Results .......................................... 109 1 3.3.4 Discussion of Analysis #1...................................................109 13.4 Analysis #2: How to Cleanup CC Data for Local Tuning? ......................... Ill 13.4.1 Design ........................................................................ Ill 13.4.2 Results ........................................................................ Ill 1 3.4.3 Discussions .................................................................. 114 13.5 Analysis #3: How Much Local Data Does an Organization Need for a Local Model? ................................................................... П4 13.5.1 Design ........................................................................ 114 1 3.5.2 Results from Analysis #3.................................................... 115 13.5.3 Checking the Analysis #3 Results ..........................................116 1 3.5.4 Discussion of Analysis #3................................................. 116 13.6 How Trustworthy Are These Results? .................................. И7 13.7 Arc These Useful in Practice or Just Number Crunching? ......................... 119 13.8 What s New on Cross-Learning? ..................................... 1 20 13.8.1 Discussion ................................................. I23 13.9 Whaťsíhe Takeaway? ............................................. 124 CHAPTER 14 Building Smarter Transfer Learners ......................................... 125 14.1 Whal Is Actually the Problem? ............................... 126 14.2 What Do We Know So Far? ..........................]] · CONTENTS xiii 14.2.1 Transfer Learning ............................................................ 128 14.2.2 Transfer Learning and SK ................................................... 128 14.2.3 Data Sel Shift ................................................................ 130 14.3 An Example Technology: THAK .................................................... 131 14.4 The Details of the Experiments...................................................... 1 35 14.4. 1 Performance Comparison ................................................... 1 35 14.4.2 Performance Measures ...................................................... 1 35 14.4.3 Retrieval Tendency .......................................................... 137 14.5 Results ................................................................................. 137 14.5.1 Performance Comparison ................................................... 137 14.5.2 Inspecting Selection Tendencies ............................................ 142 14.6 Discussion ............................................................................. 1 45 14.7 What Are the Takeaways? ............................................................ 146 CHAPTER 15 Sharing Less Data (Is a Good Thing) ........................................147 15.1 Can We Share Less Data? ............................................................ 148 15.2 Using Less Data ...................................................................... 151 15.3 Why Share Less Data? ............................................................... 156 15.3.1 Less Data Is More Reliable ................................................. 156 15.3.2 Less Data Is Faster to Discuss .............................................. 156 1 5.3.3 Less Data Is Hasier to Process .............................................. 1 57 15.4 How to Find Less Data ............................................................... 158 15.4.1 Input .......................................................................... 159 1 5.4.2 Comparisons to Other learners ............................................. 162 15.4.3 Reporting the Results ........................................................ 1 62 1 5.4.4 Discussion of Results ........................................................ 1 63 15.5 What s Next? .......................................................................... 164 CHAPTER 16 How to Keep Your Data Private ...............................................165 16.1 Motivation ............................................................................. 166 16.2 What Is PPDP and Why Is It Important? ............................................ 166 16.3 What Is Considered a Breach of Privacy? ........................................... 168 16.4 How to Avoid Privacy Breaches? .................................................... 169 1 6.4. 1 Generalization and Suppression ............................................ 1 69 1 6.4.2 Anatomization and Permutation ............................................ 171 16.4.3 Perturbalion .................................................................. 171 1 6.4.4 ( )utput Perturbalion .......................................................... 171 16.5 How Are Privacy-Preserving Algorithms Hvalualed? ............................... 172 16.5.1 Privacy Metrics .............................................................. 172 1 6.5.2 Modeling the Background Knowledge of an Attacker ..................... 173 χ* CONTENTS 16.6 Casc Siudy : Privacy and Cross-Company Defect Prediction ....................... 174 16.6.1 Results and Contributions ................................................... 177 16.6.2 Privacy and CCDP ........................................................... 177 16.6.3 CLIFF ........................................................................ 178 16.6.4 MORPH ...................................................................... l8° 16.6.5 Example of CLIFF&MORPH ............................................... 1 6.6.6 Evaluation Metrics ........................................................... 1 8 J 16.6.7 Evaluating Utility via Classification ........................................181 16.6.8 Evaluating Privatization ..................................................... 184 16.6.9 Experiments .................................................................. 185 lõAIODcsign ........................................................................ 185 16.6.11 Defect Predictors ............................................................ 185 і 6.6. 1 2 Query Generator ............................................................. 186 16.6.13 Benchmark Privacy Algorithms ............................................. 187 16.6. 1 4 Experimental Evaluation .................................................... 188 16.6.15 Discussion .................................................................... 194 16.6.16 Related Work: Privacy in SE ................................................ 195 1 6.6.17 Summary .....................................................................196 CHAPTER 17 Compensating for Missing Data ..............................................197 17.1 Background Notes on SEE and Instance Selection ................................. 199 17.1.1 Software Hffort Estimation .................................................. 199 17.1.2 Instance Selection in SEE ................................................... 199 17.2 Data Sets and Performance Measures ...............................................200 17.2.1 Data Sets .....................................................................200 17.2.2 Error Mcasu res ...............................................................203 17.3 Experimental Conditions .............................................................205 17.3.1 The Algorithms Adopted ....................................................205 17.3.2 Proposed Method: POPI ....................................................206 17.3.3 Experiments ............................................................. 208 17.4 Results ............................................................ 208 17.4. 1 Results Without Instance Selection .........................................208 17.4.2 Results with Instance Selection .............................................210 17.5 Summary ...................................................... 21 1 CHAPTER 18 Active Learning: Learning More with Less ................................213 18.1 How Docs the QUICK Algorithm Work? .................................. 215 18.1.1 Getting Rid of Similar Features: Synonym Pruning .......................215 18.1.2 Getting Rid of Dissimilar Instances: Outlier Pruning ......................216 1&2 Notes on Active Learning ......................................... 217 CONTENTS xv 18.3 The Application and Implementation Details of QUICK ...........................218 18.3. 1 Phase I : Synonym Pruning ..................................................218 18.3.2 Phase 2: Outlier Removal and Estimation ..................................219 18.3.3 Seeing QUICK in Action with a Toy Example .............................221 18.4 How the Experiments Are Designed .................................................225 18.5 Results .................................................................................227 18.5.1 Performance .................................................................. 228 18.5.2 Reduction via Synonym and Outlier Pruning .............................. 228 18.5.3 Comparison of QUICK vs. CART .......................................... 229 1 8.5.4 Detailed Look at the Statistical Analysis ................................... 230 18.5.5 Early Results on Defect Data Sets ..........................................230 18.6 Summary .............................................................................. 234 PART IV SHARING MODELS _______________________________________ CHAPTER 19 Sharing Models: Challenges and Methods ................................237 CHAPTER 20 Ensembles of Learning Machines ............................................239 20.1 When and Why Ensembles Work ....................................................240 20.1.1 Intuition ...................................................................... 241 20.1.2 Theoretical Foundation ...................................................... 24 1 20.2 Bootstrap Aggregating ( Bagging) ....................................................243 20.2.1 How Bagging Works ........................................................ 243 20.2.2 When and Why Bagging Works ............................................ 244 20.2.3 Potential Advantages of Bagging for Slili .................................. 245 20.3 Regression Trees ( RTs) for Bagging ................................................. 246 20.4 Evaluation Framework ............................................................... 246 20.4. 1 Choice of Data Sets and Preprocessing Techniques ....................... 247 20.4.2 Choice of 1 .earning Machines ............................................... 25 1 20.4.3 Choice of Evaluation Methods .............................................. 253 20.4.4 Choice of Parameters ........................................................ 255 20.5 Evaluation of Bagging + RTs in SEE ................................................255 20.5.1 Friedman Ranking ........................................................... 256 20.5.2 Approaches Most Often Ranked First or Second in Tenns of MAE. MMRE and PRED(25) .................................... 258 20.5.3 Magnitude of Performance Against (he Best ............................... 260 20.5.4 Discussion .................................................................... 261 20.6 Further Understanding of Bagging + RTs in SEF. ................................... 262 20.7 Summary .............................................................................. 264 CHAPTER 21 How to Adapt Models in a Dynamic World ................................267 21.1 Cross-Company Data and Questions Tackled ....................................... 268 21.2 Related Work ......................................................................... 270 xvi CONTENTS 2 1 .2. 1 SEH Literature on Chronology and Changing Environments .............270 21.2.2 Machine Learning Literature on Online Learning in Changing Environments ..................................................... 21-3 Formulation of the Prohlem .......................................................... ΔΙ:> 21.4 Databases ............................................................................. 274 21.4.1 ISBSG Databases ............................................................ 274 21.4.2 CocNasaCocSI .............................................................. 275 21.4.3 KitchenMax .................................................................. 276 21.5 Potential Benefit of CC Data ......................................................... 277 2 1. 5.1 Experimental Setup .......................................................... 277 21.5.2 Analysis ...................................................................... 278 21.6 Making Better Use of CC Data ......................................................280 21.7 Experimental Analysis ...............................................................282 21.7. 1 Experimental Setup ..........................................................283 21.7.2 Analysis ......................................................................284 21.8 Discussion and Implications .........................................................289 21.9 Summary ..............................................................................289 CHAPTER 22 Complexity: Using Assemblies off Multiple Models .....................291 22.1 Ensemble of Methods ................................................................293 22.2 Solo Methods and Muliimethods ....................................................294 22.2.1 Multimethods ................................................................294 22.2.2 Ninety Solo Methods ........................................................294 22.2.3 Experimental Conditions ....................................................297 22.3 Methodology ..........................................................................299 22.3. 1 Focus on Superior Methods .................................................299 22.3.2 Bringing Superior Solo Methods into Ensembles ..........................300 22.4 Results .................................................................................301 22.5 Summary .............................................................................. ЗОЗ CHAPTER 23 The Importance off Goals in Model-Based Reasoning ..................305 23.1 Introduction ............................................................ 305 23.2 Value-Based Modeling ................................................ 306 23.2.1 Biases and Models ......................................... 3О6 23.2.2 The Problem with Exploring Values ............................. ЗО6 23.3 Setting Up ......................................................... 3j0 23.3.1 Representing Value Propositions .................................. 3IO 23.4 Details ........................................................!; 312 23.4.1 Project Options: Ρ ....................................... 313 23.4.2 Tuning Options: 7......................................... CONTENTS xvii 23.5 Λη Experiment ........................................................................315 23.5.1 Case Studies: p C P .........................................................315 23.5.2 Search Methods ..............................................................317 23.6 Inside lhe Models .....................................................................318 23.7 Results .................................................................................319 23.8 Discussion ............................................................................. 320 CHAPTER 24 Using Goals in Model-Based Reasoning ...................................321 24.1 Multilayer Pereeptrons ...............................................................324 24.2 Multiobjective Evolutionary Algorithms ............................................ 326 24.3 HaD-MOEA ..........................................................................330 24.4 Using MOEAs for Creating SEE Models ...........................................331 24.4. 1 Multiobjective Formulation of the Problem ................................332 24.4.2 SEE Models Generated ......................................................333 24.4.3 Representation and Variation Operators ....................................333 24.4.4 Using the Solutions Produced by а МОВА ................................ 334 24.5 Experimental Setup ...................................................................335 24.6 The Relationship Among Different Performance Measures ........................ 339 24.7 Ensembles Based on Concurrent Optimization of Performance Measures ........ 342 24.8 Emphasizing Particular Performance Measures ..................................... 346 24.9 Further Analysis of the Model Choice ............................................... 34S 24.10 Comparison Against Other Types of Models ........................................ 348 24.11 S u m m a ry.............................................................................. 353 CHAPTER 25 A Final Word .......................................................................355 Bibliography ............................................................................................. 357
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title	Sharing data and models in software engineering
title_auth	Sharing data and models in software engineering
title_exact_search	Sharing data and models in software engineering
title_full	Sharing data and models in software engineering Tim Menzies ...
title_fullStr	Sharing data and models in software engineering Tim Menzies ...
title_full_unstemmed	Sharing data and models in software engineering Tim Menzies ...
title_short	Sharing data and models in software engineering
title_sort	sharing data and models in software engineering
topic	Software Engineering (DE-588)4116521-4 gnd Verteiltes System (DE-588)4238872-7 gnd Modellierung (DE-588)4170297-9 gnd
topic_facet	Software Engineering Verteiltes System Modellierung
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