Making software: what really works, and why we believe it
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Beijing [u.a.]
O'Reilly
2011
|
| Ausgabe: | 1. ed. |
| Schriftenreihe: | Theory in practice
|
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3501333&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020809697&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Umfang: | XV, 602 S. graph. Darst. 24 cm |
| ISBN: | 9780596808327 |
Internformat
MARC
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| 245 | 1 | 0 | |a Making software |b what really works, and why we believe it |c ed. by Andy Oram ... |
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Datensatz im Suchindex
| _version_ | 1819324169367584768 |
|---|---|
| adam_text | CONTENTS
PREFACE
xi
Part One GENERAL
PRINCI PLES
OF SEARCHING FOR AND USING EVIDENCE
1
THE QUEST FOR CONVINCING EVIDENCE
3
by Tim Menzies and Forrest
Shuìì
In the Beginning
4
The State of Evidence Today
4
Change We Can Believe In
8
The Effect of Context
10
Looking Toward the Future
11
2
CREDIBILITY, OR WHY SHOULD I INSIST ON BEING CONVINCED?
17
by
Lutz
Precheìt
and Marian
Petre
How Evidence Turns Up in Software Engineering
17
Credibility and Relevance
19
Aggregating Evidence
22
Types of Evidence and Their Strengths and Weaknesses
25
Society, Culture, Software Engineering, and You
32
Acknowledgments
33
3
WHAT WE CAN LEARN FROM SYSTEMATIC REVIEWS
35
by Barbara Kitchenham
An Overview of Systematic Reviews
36
The Strengths and Weaknesses of Systematic Reviews
39
Systematic Reviews in Software Engineering
44
Conclusion
49
4
UNDERSTANDING SOFTWARE ENGINEERING THROUGH QUALITATIVE METHODS
55
by Andrew
Ko
What Are Qualitative Methods?
56
Reading Qualitative Research
58
Using Qualitative Methods in Practice
60
Generalizing from Qualitative Results
62
Qualitative Methods Are Systematic
62
5
LEARNING THROUGH APPLICATION: THE MATURING OF THE QIP IN THE
SEL
65
by Victor R.
Basili
What Makes Software Engineering Uniquely Hard to Research
65
A Realistic Approach to Empirical Research 66
The NASA Software Engineering Laboratory: A Vibrant
Testbed
for Empirical Research
67
The Quality
Im
provement Pa
rad i gm 69
Conclusion
PERSONALITY, INTELLIGENCE, AND EXPERTISE: IMPACTS ON SOFTWARE DEVELOPMENT
79
byJoE.Hannay
How to Recognize Good Programmers 81
Individual or Environment
Concluding Remarks 101
7
WHY IS IT SO HARD TO LEARN TO PROGRAM?
by Mark Guzdiai
Do Students Have Difficulty Learning to Program? 112
What Do People Understand Naturally About Programming?
Making the Tools Better by Shifting to Visual Programming
Contextualizing for Motivation 118
Conclusion: A Fledgling Field 121
8
BEYOND LINES OF CODE: DO WE NEED MORE COMPLEXITY METRICS?
125
by Israel Herraiz and Ahmed E. Hassan
Surveying Software 2
Measuring the Source Code
A Sample Measurement
128
Statistical Analysis
133
Some Comments on the Statistical Methodology
139
So Do We Need More Complexity Metrics?
140
Fari
Two SPECif
1С
TOPICS IN SOFTWARE ENGINEERING
_________________________________________
9
AN AUTOMATED FAULT PREDICTION SYSTEM H5
by
f
ìainej.
Weyuker and Thomas
J.
Ostrand
Fault Distribution
146
Characteristics of Faulty Files
149
Overview of the Prediction Model
150
Replication and Variations of the Prediction Model
151
Building a Tool
157
The Warning Label
157
10
ARCHITECTING: HOW MUCH AND WHEN?
161
by
ßarry
Boehm
Does the Cost of Fixing Software Increase over
lhe
Project Life Cycle?
162
How Much Architecting Is Enough?
162
Using What We Can Learn from Cost-to-Fix Data About the Value of Architecting
165
So How Much Architecting Is Enough?
178
Does the Architecting Need to Be Done Up Front?
181
Conclusions
wl CONTENTS
11
CONWAY S COROLLARY
187
by Christian Bird
Conway s Law
187
Coordination, Congruence, and Productivity
189
Organizational Complexity Within Microsoft
194
Chapels in the Bazaar of Open Source Software
201
Conclusions
205
12
HOW EFFECTIVE IS TEST-DRIVEN DEVELOPMENT?
207
by
Burak
Turhan,
Lucas Layman,
Madeìine
Diep,
Hakan Erdo$mus,
and Forrest Shuil
The TDD Pill—What Is It?
208
Summary of Clinical TDD Trials
209
The Effectiveness of TDD
211
Enforcing Correct TDD Dosage in Trials
214
Cautions and Side Effects
215
Conclusions
216
Acknowledgments
217
13
WHY AREN T MORE WOMEN IN COMPUTER SCIENCE?
221
by
Michele
A, Wbitecraft and Wendy
M. Williams
Why So Few Women?
222
Should We Care?
227
Conclusion
234
14
TWO COMPARISONS OF PROGRAMMING LANGUAGES
239
by
Lutz
řrecheit
A Language Shoot-Out over a Peculiar Search Algorithm
240
Plat_Forms: Web Development Technologies and Cultures
248
So What?
257
15
QUALITY WARS: OPEN SOURCE VERSUS PROPRIETARY SOFTWARE
259
by Diomidis Spinellis
Past Skirmishes
260
The Battlefield
261
Into the Battle
265
Outcome and Aftermath
286
Acknowledgments and Disclosure of Interest
289
16
CODE TALKERS
295
by Robert
DeLine
A Day in the Life of a Programmer
295
What Is All This Talk About?
298
A Model for Thinking About Communication
307
17
PAIR PROGRAMMING
311
by Laurie Williams
A History of Pair Programming
312
CONTENTS vH
Pair Programming in an Industrial Setting 314
Pair Programming in an Educational Setting 317
Distributed Pair Programming 319
Challenges 320
Lessons Learned
Acknowledgments 322
18
MODERN CODE REVIEW 329
by Jason Cohen
Common Sense 329
A Developer Does a Little Code Review 330
Group Dynamics 33**
Conclusion 336
19
A COMMUNAL WORKSHOP OR DOORS THAT CLOSE?
by )or$e Aranda
Doors That Close 339
A Communal Workshop 342
Work Patterns
345
One More Thing...
347
20
IDENTIFYING AND MANAGING DEPENDENCIES IN GLOBAL SOFTWARE DEVELOPMENT
349
by
Marcelo Cataìdo
Why Is Coordination a Challenge in GSD?
350
Dependencies and Their Socio-Technical Duality
351
From Research to Practice
362
Future Directions
366
21
HOW EFFECTIVE IS MODULARIZATION?
373
by Neil Thomas and
Caii
Murphy
The Systems
374
What Is a Change?
376
What Is a Module?
381
The Results
383
Threats to Validity
389
Summary
390
22
THE EVIDENCE FOR DESIGN PATTERNS
393
by Walter
Tichy
Design Pattern Examples
394
Why Might Design Patterns Work?
397
The First Experiment: Testing Pattern Documentation
398
The Second Experiment: Comparing Pattern Solutions to Simpler Ones
403
The Third Experiment: Patterns in Team Communication
407
Lessons Learned
410
Conclusions
412
Acknowledgments
413
Wli CONTENTS
23
EVIDENCE-BASED FAILURE
PREDICTION
415
by Nachiappan Na$appan and Thomas
Baii
Introduction
416
Code Coverage
417
Code Churn
418
Code Complexity
421
Code Dependencies
422
People and Organizational Measures
423
Integrated Approach for Prediction of Failures
426
Summary
430
Acknowledgments
432
24
THE ART OF COLLECTING BUG REPORTS
435
by Rahul Premraj and Thomas
Zimmermann
Good and Bad Bug Reports
436
What Makes a Good Bug Report?
437
Survey Results
439
Evidence for an Information Mismatch
441
Problems with Bug Reports
444
The Value of Duplicate Bug Reports
445
Not All Bug Reports Get Fixed
448
Conclusions
449
Acknowledgments
450
25
WHERE DO MOST SOFTWARE FLAWS COME FROM?
453
by Deiuayne Perry
Studying Software Flaws
454
Context of the Study
455
Phase
1:
Overall Survey
456
Phase
2:
Design/Code Fault Survey
462
What Should You Believe About These Results?
486
What Have We Learned?
490
Acknowledgments
492
26
NOVICE PROFESSIONALS: RECENT GRADUATES IN A FIRST SOFTWARE ENGINEERING JOB
495
by
Andreu)
Be$eì
and Beth Simon
Study Methodology
497
Software Development Task
501
Strengths and Weaknesses of Novice Software Developers
505
Reflections
507
Misconceptions That Hinder Learning
509
Reflecting on Pedagogy
510
Implications for Change
512
27
MINING YOUR OWN EVIDENCE
517
by Kim Sebastian
Herzig
and Andreas
Zeiler
What Is There to Mine?
518
CONTENTS
ta
Designing a Study
518
A Mining Primer
519
Where to Go from Here
526
Acknowledgments
528
26
COPY-PASTE AS A PRINCIPLED ENGINEERING TOOL
531
by Michael Godfrey and Cory Kapser
An Example of Code Cloning
532
Detecting Clones in Software
533
Investigating the Practice of Code Cloning
535
Our Study
540
Conclusions
543
29
HOW USABLE ARE YOUR APIS?
545
by Steoen Clarke
Why s Important to Study API Usability?
546
First Attempts at Studying API Usability
548
If At First You Don t Succeed...
552
Adapting to Different Work Styles
559
Conclusion
563
ЗО
WHAT DOES 10X MEAN? MEASURING VARIATIONS IN PROGRAMMER PRODUCTIVITY
567
by
Steve McConnell
Individual Productivity Variation in Software Development
567
Issues in Measuring Productivity of Individual Programmers
569
Team Productivity Variation in Software Development
571
A CONTRIBUTORS
575
INDEX 587
|
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| title | Making software what really works, and why we believe it |
| title_auth | Making software what really works, and why we believe it |
| title_exact_search | Making software what really works, and why we believe it |
| title_full | Making software what really works, and why we believe it ed. by Andy Oram ... |
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| title_full_unstemmed | Making software what really works, and why we believe it ed. by Andy Oram ... |
| title_short | Making software |
| title_sort | making software what really works and why we believe it |
| title_sub | what really works, and why we believe it |
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