But Does It Actually Work? AutomaCon 2015, Portland Fletcher Nichol @fnichol 

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@fnichol 

–Me, often “Infrastructure code has high entropy” 

–Me, also “Infrastructure code must be executed frequently to prevent it from degrading over time” 

–Devil’s Advocate Me “But, why?” 

–Adam Jacob, Web Operations “Enable the reconstruction of the business from nothing but a source code repository, an application data backup, and bare metal resources.” Infrastructure as Code 

–Fletcher, This Talk “It is software.” Infrastructure as Code 

https://flic.kr/p/dp6D4X 

Does Code Decay? • Published January, 2001 • Large codebase for telephone switches • 100,000,000 LOC (C/C++) • 15-year old codebase • Each release ~20,000,000 LOC 

Does Code Decay? Metaphor: software suffering from decay can be thought of as diseased 

Decayed Code Unit of code is decayed if it is harder to change than it should be, measured in terms of effort, interval, and quality 

Decayed Code: Measurement 1. Cost of the change, which is effectively on the personnel cost for the developers who implemented it 2. Interval to complete the change, the calendar/clock time required 3. Quality of the changed software 

Code Decay • Decay is temporal, may be useful to add a “more difficult to change than it used to be” to the definition • Code can be ”correct” and still be decayed • Software that is decaying is simultaneously increasing in value 

Article Claim “Code decay is the result of previous changes to the software” 

Decay: Three Classes of Changes 1. Adaptive changes - add new functionality to a system 2. Corrective changes - fix faults in the software 3. Perfective changes (“maintenance for the sake of maintenance” or “reengineering”) - are intended to improve the developers’ ability to maintain the software without altering functionality or fixing faults 

Symptoms of Code Decay (Selected) 1. Excessively complex (bloated) code 2. A history of frequent change (a.k.a. code churn) 3. Widely dispersed changes 4. Numerous interfaces (i.e. entry points) 

But It Gets Worse • In general, low fault tolerance (everyone read Release It!) • Dependency management complexities • Aggressively changing runtimes 

Shannon Entropy • From Information theory • Introduced by Claude E. Shannon in "A Mathematical Theory of Communication” (1948) • Expected value of the information contained in each message received • Entropy of a message is its amount of uncertainty • Increases when the message is closer to random • Decreases when it is less random 

Entropy Example Polling on a political issue 

Entropy Example Coin toss 

Entropy and Software Systems • Published May, 2011 • Combines entropy and testing concepts 

Why Do We Test? We test software systems because there is uncertainty in its actual behavior. 

Why Do We Test? 1. The more we test, the more we know about the system under test 2. The more we know about the system under test, the more likely faults can be found 

Infrequent Testing 0 22.5 45 67.5 90 Time Entropy 

Frequent Testing 0 20 40 60 80 Time Entropy 

Frequent Testing 0 20 40 60 80 Time Entropy 

Frequent Execution 0 20 40 60 80 Time Entropy 

Frequent Execution 0 20 40 60 80 Time Uncertainty Code in use Code in decay 

–Jez Humble and David Farley, Continuous Delivery “If it hurts, do it more frequently, and bring the pain forward.” 

–Me, again “Infrastructure code must be executed frequently to prevent it from degrading over time” 

Thank You! AutomaCon 2015, Portland Fletcher Nichol @fnichol 

References Jacob, Adam. “Infrastructure as Code.” Web Operations: Keeping the Data on Time. Ed. John Allspaw and Jesse Robbins. Beijing: O’Reilly, 2010. 65-80. Print. Boehm, B.w., and P.n. Papaccio. “Understanding and Controlling Software Costs.” IEEE Transactions on Software Engineering IIEEE Trans. Software Eng. 14.10 (1988): 1462-477. Web. 9 Sept. 2015. Eick, S.g., T.l. Graves, A.f. Karr, J.s. Marron, and A. Mockus. “Does Code Decay? Assessing the Evidence from Change Management Data.” IEEE Transactions on Software Engineering IIEEE Trans. Software Eng. 27.1 (2001): 1-12. Web. 9 Sept. 2015. Yang, Linmin. “Entropy and Software Systems: Towards an Information-theoretic Foundation of Software Testing.” Diss. Washington State U, 2011. Web. 9 Sept. 2015. . “Entropy (information theory).” Wikipedia: The Free Encyclopedia. Wikimedia Foundation, Inc. 7 Sept. 2015. 9 Sept. 2015 . Humble, Jez, and David Farley. Continuous Delivery. Upper Saddle River, NJ: Addison-Wesley, 2011. Print. 

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