Software Engineering: the war against complexity
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- Priscilla Miles
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1 Software Analysis And Transformation Software Engineering: the war against complexity Jurgen J. Vinju Centrum Wiskunde & Informatica (CWI) CHAQ Change-centric Quality Assurance open tool demonstrations event at Antwerp University on February 24th, 2015
2 CWI SWAT CWI SWAT
3 Douglas DC-2 KLM Uiver
4 Great Design We want great design for software too trustworthy cheap versatile simply beautiful
5 Great Design need We want great design for software too trustworthy cheap versatile simply beautiful
6 Great Design The DC-2 is obviously a high-quality design it does not crash and handles very well it does not wear quickly yet, it is easy to maintain it s a small investment compared to what you can earn with it it can take on any cargo, including passengers, comfortably it s both good in general and good in detail; every detail matters it s very, very shiny We know pretty well how to describe, judge and improve airplan quality
7 Software Design Most software does not have to actually fly, so it s not as hard to design as the DC-2 Common belief that software is indeed soft Ugly software also works If software breaks, we just fix it We know this is not true
8 Software Design Most software does not have to actually fly, so it s not as hard to design as the DC-2 So, what exactly is Common belief that software is indeed soft Ugly software also works and If software breaks, we just fix it good software design? why does it matter? We know this is not true
9 Software quality is hard to observe
10 Software quality is hard to observe if you can t see it, it does not mean it does not exist
11 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague
12 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light
13 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger
14 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art
15 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software
16 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software consists of many small details
17 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software consists of many small details evolves slowly but surely
18 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software consists of many small details evolves slowly but surely too big to read and too fast to trace
19 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software consists of many small details evolves slowly but surely too big to read and too fast to trace new concept to most people
20 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger too subjective, like art Software consists of many small details evolves slowly but surely too big to read and too fast to trace new concept to most people quality is contextual
21 Software quality is hard to observe if you can t see it, it does not mean it does not exist too small or too slow, like the black plague too big or too fast, like the earth and the speed of light too scary, like a stranger Agenda make software quality known to and too subjective, like art observable by non-software-specialists, creating more Software traction for investing in software quality consists of many small details evolves slowly but surely too big to read and too fast to trace new concept to most people quality is contextual
22 Complexity Dominates Software Quality Software quality is about subjective requirements correct, testable, efficient, secure, flexible, but all of these depend on COMPLEXITY ( simplicity)
23 Complexity Trumps Correctness & security: can t verify what you can t define debilitating high cost Testable: can t test what s not independend Efficiency: can t pin-point causes of bottlenecks Flexible: can t predict impact of change
24 Software Complexity Agenda
25 Software Complexity Agenda Philosophy (what is software complexity?)
26 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?)
27 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?):
28 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration
29 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration 3 examples
30 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) 3 examples Construction (how can we prevent it?): Conclusion (holistic perspective) one-stop-shop advertisement Meta-tools Public/private collaboration
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34 If Kafka would write a book today This kind of software exists everywhere: 10K to 25M lines of code 2 to 10 programming languages and dialects 20 to 200 dependencies on library components and frameworks 10 to 1000 programmers 1 to 1M users 10 to 40 years lifetime IT happens having a nightmarishly complex, bizarre, or illogical quality
35 Software at scale
36 Software at scale Common but hard questions are:
37 Software at scale Common but hard questions are: How can this have worked, ever?
38 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why?
39 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced?
40 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk?
41 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk?
42 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk? Common situations are:
43 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk? Common situations are: lack of control leading to unbounded growth
44 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk? Common situations are: lack of control leading to unbounded growth lack of predictability, leading to unbounded cost
45 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk? Common situations are: lack of control leading to unbounded growth lack of predictability, leading to unbounded cost lack of long term perspective, leading to ill-informed decisions
46 Software at scale Common but hard questions are: How can this have worked, ever? What is it? What does it do? When? How? Why? Can it be fixed, extended, modified, replaced? At what cost? At what risk? Common situations are: lack of control leading to unbounded growth lack of predictability, leading to unbounded cost lack of long term perspective, leading to ill-informed decisions complex software is the enemy of quality
47 Software at scale Software Complexity is exhibited by: heterogeneity (different kinds of parts) code volume (textually) dependence (semantics) encapsulation (nesting) distribution (deployment) evolution (versions) Material Time Time Time Time Space Space Space Space
48 Complex or Complicated? Complicated = many interrelated parts linear: small change = small impact predictable: straight flow, local failure decomposable: manageable Complex = unpredictable & hard to manage emergent: whole is more than sum non-linear: small change = big impact? cascading failure hysteresis: you must understand its history indivisible [CSIS paper: "Organizing for a Complex World: The Way Ahead]
49 Complex or Complicated? Complicated = many interrelated parts linear: small change = small impact predictable: straight flow, local failure decomposable: manageable Complex = unpredictable & hard to manage emergent: whole is more than sum non-linear: small change = big impact? cascading failure Software systems may generate complex behaviors, but the code should not exhibit complex attributes hysteresis: you must understand its history indivisible [CSIS paper: "Organizing for a Complex World: The Way Ahead]
50 Software Complexity Agenda
51 Software Complexity Agenda Philosophy (what is software complexity?)
52 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?)
53 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?):
54 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration
55 Science Code & Info Extraction Analysis Data & Stats Visualization Plots
56 Science Software Analytics Code & Info Extraction Analysis Data & Stats Visualization Plots
57 Science Software Analytics debunking common beliefs Code & Info Extraction Analysis Data & Stats Visualization Plots
58 Science Software Analytics debunking common beliefs discovering new truths by observation/experimentation Code & Info Extraction Analysis Data & Stats Visualization Plots
59 Science Software Analytics debunking common beliefs discovering new truths by observation/experimentation mining software repositories! Code & Info Extraction Analysis Data & Stats Visualization Plots
60 Science of SLOC & CC Source Lines of Code (SLOC) a measure of volume indicating effort of reading and writing, complexity Davy Landman, ICSM2014 Submitted to JSEP Cyclomatic Complexity (CC) linearly independent control flow paths (how many splitting points) a measure of testing effort (test cases needed to cover all blocks) indicating effort of understanding, complexity, maybe
61 Science of SLOC & CC Hypothesis: SLOC = a * CC + b? both a measure of volume? which other dimension? should we even measure both???? Literature on this on smaller corpora answer yes answer yes, when summed up to the file level answer yes, if we apply logarithmic transformations Let s check this. because in theory a lot more code is possible because repeated sum (multiplication) is the essence of linearity
62 Scatter plots R2 = 0.4 variance increases 17.6 million methods
63 Transformations and Aggregation Summing CC to file level R2 = 0.7 variance still increases Sum makes correlation better A/B test shows that aggregation is indeed a cause of strong correlation
64 The truth about CC/SLOC No linear correlation Dissappointing" truth Actionable keep on measuring CC! Avoided the interpretation of CC Extraction Analysis Code Data & Stats see [SCAM2012] and [Abran 06] Visualization Application Software Improvement Group Plots
65 Software Complexity Agenda
66 Software Complexity Agenda Philosophy (what is software complexity?)
67 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?)
68 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?):
69 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration
70 Maintenance Activities: Reverse engineering Re-engineering Visualization Refactoring understanding specimens about efficiency and effectivity tools for getting it right, faster tools for mitigating complexity Transformation Extraction Analysis Visualization Code Model Picture Generation
71 Transformation Refactoring is improving internal quality reducing complexity Code Model without changing functionality. Picture
72 [ Joshua Kerievsky, industriallogic.com]
73 Refactoring Tools help by: analyzing conditions transforming everywhere user interactions Transformation Extraction Code Genera preview undo Analysis Visualization Model Picture
74 Refactoring Tools help by: analyzing conditions transforming everywhere user interactions Transformation Extraction Code Genera preview undo Analysis Visualization Model The value and heavy lifting is in the highly detailed model of programming language syntax, static and dynamic semantics Picture
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77 Many interesting refactorings tools in IDEs are broken due to language evolution
78 Many interesting refactorings tools in IDEs are broken due to language evolution Most refactorings do not guarantee correctness in the context of multi-threading [Schäfer, ECOOP2010]
79 Many interesting refactorings tools in IDEs are broken due to language evolution Most refactorings do not guarantee correctness in the context of multi-threading [Schäfer, ECOOP2010] Ongoing work; Maria Gouseti
80 [Schäffer 2010]
81 value Equivalence complex check complex Java reuse SIMPLICITY Intermediate (Synchronized) Flow Program Refactored Flow Program C# sourceto-source sourceto-source 20 pages of code, 600 lines of code [Rascal]
82 vs Refactoring can tools help improving quality They are complicated First simplify the tools Then simplify the code
83 vs Refactoring can tools help improving quality They are complicated First simplify the tools Then simplify the code What if programmers spend less time on debugging accidental problems and spend it on hard features for business value instead?
84 Software Complexity Agenda
85 Software Complexity Agenda Philosophy (what is software complexity?)
86 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?)
87 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?):
88 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration
89 Construction Correct-by-construction Variability by prediction Transformation Code Model Driven Engineering Generation Software Architecture Analysis Model Formal Methods Formalization Programming languages Design make better software
90 Domain Specific Languages Requirements=domain analysis Separate what is fixed from what is variable (predict) Code Language for domain experts No accidental complexity Analysis Model Generation Formalization Multiple back-ends Picture Technology evolution Different Audiences
91 Digital Forensics [Jeroen van den Bos, Tijs van der Storm] Digital evidence is messy Technology is highly variable (cameras, formats) Evidence needs to be collected from terabytes within days
92 Derric Language
93 Derric Results Just as fast or faster than hand-optimized C++ code Derric definitions retargeted to other algorithms Derric definitions transformed for speed trade-offs [ICSE 11, ICMT 12,ECFMA 13]
94 Software Complexity Agenda
95 Software Complexity Agenda Philosophy (what is software complexity?)
96 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?)
97 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?):
98 Software Complexity Agenda Philosophy (what is software complexity?) Science (what is the truth about software complexity?) Engineering Maintenance (what can we do about it?) Construction (how can we prevent it?): Conclusion (holistic perspective) Meta-tools Public/private collaboration
99 Holistic & Reflective Key: software which reads and writes software Science Maintenance Construction Meta Domain tools share similar character transfer theory to industry transfer knowledge to research
100 Symbiosis
101 Symbiosis Maintenance and Construction need scientific and industrial validation Maintenance and Construction need input from Mining Science needs what if scenarios; hypotheses Maintenance and Construction need programming language models, analysis, visualization, generation, Industry needs predictions, tools, expert engineers Academia needs data, domain expertise and researchers
102 Public/Private collaboration Tools enable exchange Research Engineering SWAT - SoftWare Analysis And and Transformation
103 Science Collaboration Portfolio Software Improvement Group OSSMETER EU Project ( (holistic quality assessment) Code (metrics), Meta-data (versions, bugs, questions), Natural language (sentiments) Maintenance Dutch Banking/Insurance companies (re-engineering, reverse engineering) High-tech industries (embedded systems, networks, television) Construction Games (EQUA project) [logo s omitted] NFI ( CSI Netherlands, evidence collection) Tax office, financial auditing companies (fraud detection) Banks (configuration, verification, modeling & simulation) High-tech industries (protocols, state machines, configuration)
104 Software Industry & Research thrive in the current climate of public/private collaboration = opportunity + responsibility
105 Software Industry & Research thrive in the current climate of public/private collaboration = opportunity + responsibility
106 Software Industry & Research thrive in the current climate of public/private collaboration = opportunity + responsibility
107 Software Industry & Research thrive in the current climate of public/private collaboration = opportunity + responsibility
108 Software Tools Research Software Engineering SWAT - SoftWare Analysis And and Transformation
109 Software Tools Research Software Engineering SWAT - SoftWare Analysis And and Transformation
110 (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
111 Languages (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
112 Languages Dialects (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
113 Languages Dialects Frameworks (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
114 Languages Dialects Frameworks Libraries (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
115 Languages Dialects Frameworks Libraries Formats (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
116 Languages Dialects Frameworks Libraries Formats no true standards (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
117 Languages Dialects Frameworks Libraries Formats no true standards challenge for meta programming (Brueghel, Tower of Babel) SWAT - SoftWare Analysis and Transformation
118 Rascal Software Tools Research Software Engineering SWAT - SoftWare Analysis And and Transformation
119 Rascal is a language for meta programming Transformatio Extractio Code Executio Generatio (which we apply for science, maintenance and construction in research and industry) Analysi Visualizatio Model Picture Formalizatio risky investment 10 year perspective Renderin Conversio SWAT - SoftWare Analysis And and Transformation
120 Conclusion
121 Software Complexity Agenda Philosophy Science Maintenance Construction Conclusion
122 Conclusion Software Complexity Agenda Philosophy Science Maintenance Construction Going meta is the key Tools enable collaboration Tools manage accidental complexity Community is necessary to mitigate cost Education needs to go meta
123 Conclusion Software Complexity Agenda Philosophy Science Maintenance Construction Going meta is the key Tools enable collaboration Tools manage accidental complexity Community is necessary to mitigate cost Education needs to go meta Let engineers focus on value
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