1 Molecular Biology 3M03 Fundamental Concepts of Development ...

Isaac Corro Ramos

Description

• cours magistral

1 Molecular Biology 3M03  Fundamental Concepts of Development      This course examines fundamental mechanisms underlying development, from an embryological  and molecular genetic point of view. Besides class material presented by Dr. Campos, Drs. Gupta,  Gillespie and West‐Mays have been invited to showcase their model organisms and research  programme.      We will begin by examining how pattern and identity is established in early embryogenesis, and  then proceed to explore how cells acquire their fate, and build the intricate morphology of the maturing  embryo. Lecture material will be complemented by laboratory demonstrations.     The first half will focus on early development using three classical model systems, sea urchins,  frogs, chicks and flies. The second half of the course will examine specific processes underlying cellular  diversity and morphogenesis and is focused on nervous system development. This will be  complemented by additional model systems in which a genetic approach to development is employed  (guest speakers).  In each of these model systems a specific hypothesis will be addressed and  experiments and current models will be discussed.    Course Instructor:  Ana Campos   Office LSB 541             e‐mail:      Office Hours:  by  appointment   Please make inquiries by e‐mail     Lectures: Tuesday and Wednesday 12:30‐13:20, ABB 136  Labs: Tuesday and Wednesday, 14:30 LSB 109 and 110    Textbook      Gilbert, Developmental Biology strongly recommended, either 9th or 8th Edition is acceptable.

• feb 28‐29  central nervous system development chapter 9
• jan 24‐25  continuation and chapter 8 chicken 
• week 4   dr.
• week 3   sea urchin i  jan 17‐18 
• week 7  dr.
• week 5   frog i or ii  jan 31‐feb 1  
• week 9  fly lab i  feb 28‐29 

Sujets

• Cours magistral
• Programme

Informations

• Publié par: iwlo0
• Nombre de lectures: 76
• Langue: English
• Poids de l'ouvrage: 3 Mo

Extrait

Statistical Procedures for Certification of
Software SystemsTHOMASSTIELTJESINSTITUTE
FORMATHEMATICS
c Corro Ramos, Isaac (2009)
A catalogue record is available from the Eindhoven University of Technology Library
ISBN: 978-90-386-2098-5
NUR: 916
Subject headings: Bayesian statistics, reliability growth models, sequential testing,
software release, software reliability, software testing, stopping time, transition sys-
tems
Mathematics Subject Classification: 62L10, 62L15, 68M15
Printed by Printservice TU/e
Cover design by Paul Verspaget
This research was supported by the Netherlands Organisation for Scientific Research
(NWO) under project number 617.023.047.Statistical Procedures for Certification of
Software Systems
proefschrift
ter verkrijging van de graad van doctor aan de
Technische Universiteit Eindhoven, op gezag van de
Rector Magnificus, prof.dr.ir. C.J. van Duijn, voor een
commissie aangewezen door het College voor
Promoties in het openbaar te verdedigen
op dinsdag 15 december 2009 om 16.00 uur
door
Isaac Corro Ramos
geboren te Sevilla, SpanjeDit proefschrift is goedgekeurd door de promotoren:
prof.dr. K.M. van Hee
en
prof.dr. R.W. van der Hofstad
Copromotor:
dr. A. Di BucchianicoContents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 The importance of software testing . . . . . . . . . . . . . . . 1
1.1.2 Software failure vs. fault . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Black-box vs. model-based testing . . . . . . . . . . . . . . . 3
1.1.4 When to stop testing . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Goal and outline of the thesis . . . . . . . . . . . . . . . . . . . . . . 4
2 Probability Models in Software Reliability and Testing 9
2.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Stochastic processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Counting processes . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Basic properties . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Property implications . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Software testing framework . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Common notation . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Reliability growth models . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Stochastic ordering and reliability growth . . . . . . . . . . . 21
2.4 Classification of software reliability growth models . . . . . . . . . . 23
2.4.1 Previous work on model classification . . . . . . . . . . . . . 23
2.4.2 Classification based on properties of stochastic processes . . . 26
2.5 General order statistics models . . . . . . . . . . . . . . . . . . . . . 27
2.5.1 Jelinski-Moranda model . . . . . . . . . . . . . . . . . . . . . 30
2.5.2 Geometric order statistics model . . . . . . . . . . . . . . . . 32
2.6 Non-homogenous Poisson process models . . . . . . . . . . . . . . . . 33
2.6.1 Goel-Okumoto model . . . . . . . . . . . . . . . . . . . . . . 35
2.6.2 Yamada S-shaped model . . . . . . . . . . . . . . . . . . . . . 36
2.6.3 Duane (power-law) model . . . . . . . . . . . . . . . . . . . . 37
2.7 Linking GOS and NHPP models . . . . . . . . . . . . . . . . . . . . 38
2.7.1 A note on NHPP-infinite models . . . . . . . . . . . . . . . . 40
2.8 Bayesian approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.9 Some other models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.9.1 Schick-Wolverton model . . . . . . . . . . . . . . . . . . . . . 42
3 Statistical Inference for Software Reliability Growth Models 45
3.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Trend analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Model type selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.4 Model estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4.1 ML estimation for GOS models . . . . . . . . . . . . . . . . . 56
Jelinski-Moranda model . . . . . . . . . . . . . . . . . . . . . 57
vvi Contents
3.4.2 ML estimation for NHPP models . . . . . . . . . . . . . . . . 58
Goel-Okumoto model . . . . . . . . . . . . . . . . . . . . . . 58
Duane (power-law) model . . . . . . . . . . . . . . . . . . . . 59
3.5 Model validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.6 Model interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 A New Statistical Software Reliability Tool 65
4.1 General remarks about the implementation . . . . . . . . . . . . . . 65
4.2 Main functionalities . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.1 Data menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2.2 Graphics menu . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2.3 Analysis menu . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2.4 Help menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3 Two examples of applying reliability growth models in software de-
velopment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3.1 Administrative software at an insurance company . . . . . . . 78
4.3.2 A closable dam operating system . . . . . . . . . . . . . . . . 83
5 Statistical Approach to Software Reliability Certification 89
5.1 Previous work on software reliability certification . . . . . . . . . . . 90
5.1.1 Certification procedure based on expected time to next failure 90
5.1.2 pro based on fault-free system . . . . . . 92
5.2 Bayesian approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.3 Bayesianreleaseprocedureforsoftwarereliabilitygrowthmodelswith
independent times between failures . . . . . . . . . . . . . . . . . . . 97
5.3.1 Jelinski-Moranda and Goel-Okumoto models . . . . . . . . . 99
Case 1: N and deterministic . . . . . . . . . . . . . . . . . 99
Case 2: N known and fixed, Gamma distributed . . . . . . 100
Case 3: N Poisson distributed, known and fixed (Goel-
Okumoto model) . . . . . . . . . . . . . . . . . . . . 102
Case 4: N Poisson and Gamma distributed (full Bayesian
approach) . . . . . . . . . . . . . . . . . . . . . . . . 103
5.3.2 Run model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Case 1: N and deterministic . . . . . . . . . . . . . . . . . 107
Case 2: N Poisson distributed, known and fixed . . . . . . 107
Case 3: N known and fixed, Beta distributed . . . . . . . . 109
Case 4: N Poisson and Beta (full Bayesian ap-
proach) . . . . . . . . . . . . . . . . . . . . . . . . . 110
6 Performance of the Certification Procedure 111
6.1 Jelinski-Moranda model . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.1 Case 1: N and deterministic . . . . . . . . . . . . . . . . . 111
6.1.2 Case 2: N known and fixed, Gamma distributed . . . . . . 112
6.1.3 Case 3: N Poisson distributed, known and fixed (Goel-
Okumoto model) . . . . . . . . . . . . . . . . . . . . . . . . . 117Contents vii
6.1.4 Case 4: N Poisson and Gamma distributed (full Bayesian
approach) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2 Run model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.2.1 Case 1: N and deterministic . . . . . . . . . . . . . . . . . 124
6.2.2 Case 2: N Poisson distributed, known and fixed . . . . . . 124
6.2.3 Case 3: N known and fixed, Beta distributed . . . . . . . . 126
6.2.4 Case 4: N Poisson and Beta (full Bayesian ap-
proach) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7 Model-Based Testing Framework 131
7.1 Labelled transition systems and a diagram technique for representation132
7.2 Example of modelling software as a labelled transition system . . . . 134
7.3 Error distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.3.1 Binomial distribution of error-marked transitions . . . . . . . 137
7.3.2 Poisson distribution ofed . . . . . . . . 139
7.4 Testing process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.5 Walking Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
7.5.1 Walking function update for labelled transition systems . . . 146
7.5.2 W update for acyclic workflow transition systems148
7.6 Common notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
8 Statistical Certification Procedures 155
8.1 Certificationprocedurebasedonthenumberofremainingerror-marked
transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
8.2 procedure based on the survival probability . . . . . . . 157
8.3 Practical application . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
8.3.1 General setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
8.3.2 Performance of the stopping rules . . . . . . . . . . . . . . . 162
9 Testing the Test Procedure 167
9.1 Generating random models . . . . . . . . . . . . . . . . . . . . . . . 167
9.2 Quality of the procedure . . . . . . . . . . . . . . . . . . . . . . . . . 170
9.3 Stresser: a tool for model-based testing certification . . . . . . . . . 173
9.3.1 Creating labelled transition systems . . . . . . . . . . . . . . 173
9.3.2 Error distribution . . . . . . . . . . . . . . . . . . . . . . . . 173
9.3.3 Parameters of testing: walk