Technical Bankruptcy

What is technical bankruptcy? More to the point, what is technical debt? And what happens to software systems when the interest payments on technical debt go out of control?

Presented CukeUp 2015

Transcript

1. technical bankruptcy lukas oberhuber simply business @lukasco 1
2. who am i? simply business online insurance 290,000 policies 45 people on tech team ~60 developing products ~100,000 lines of code increasing rapidly agile build-measure-learn 2
3. our story new shareholder with little technology experience had to explain replacing 5 year old system: couldn’t improve business and it’s taken 2 years needed to understand tech debt and tech bankruptcy (they weren’t big fans of ‘the software is never done’) 3
4. our fix old system: 1 week to make rating change new system: 1 hour and done by product manager 4
5. conclusion actual bankruptcy due to technical bankruptcy is likely high examples friendster video management system at the bbc (cancelled) new air traffic system in the usa npfit healthcare software programme here at home 5
6. conclusion equations that can help us reason about how much debt we are accumulating and paying off how that increases the effort to create change 6
7. conclusion same behaviours in business and organisations examples signal failures on the london tube poorly flushing toilets at simply business non-earthquake safe bridges strikes examples of bankruptcy iraqi army garment factory that collapsed in bangladesh worldwide corruption 7
8. conclusion couched as software you can transfer to different domain looking for an equation hints at the end on how to deal with tech debt 8
9. conclusion at the end of this, you should be scared 9
10. but first some definitions 10
11. definition of technical bankruptcy economic cost of to change system exceeds needed return for most changes even if system is working and stable implicit: change is needed implicit: software is never done 11
12. the problem usually recognised long after bankruptcy has occurred it is too late to fix [imagine flossing your teeth vigorously when a root canal is needed] businesses fail because of it [example: friendster] 12
13. definition of technical debt the work that hasn’t been done difference between doing something properly and the less correct way it was actually done quantified by: amount of work left to correct the implementation vs original implementation not: every possible option that could be built 13
14. problem with technical debt makes good solutions bad increases cost of change starts slowly and picks up speed eventually, if not paid off, leads to tech bankruptcy 14
15. intuition around technical debt principle needs to be paid off interest accrues how fast? how bad? 15
16. definition of complexity a unit of the final product examples tech lines of code rows in a database function points business/organisation/other employees paragraphs in laws customers 16
17. caveat: it’s all a hypothesis based on empirical, mathematical information from similar or adjacent areas a thought experiment further investigation warranted 17
18. debt and complexity: two angles complexity is the base (good) debt is the accelerant (makes it worse) equation: effort to make 1 unit of change 18
19. what does debt look like? the evidence beyond simply business debt makes good code bad; what does bad look like? focusing on maintenance (not exactly the same as adding a line/unit), so bear with the shift credit: Capers Jones http://www.compaid.com/caiinternet/ezine/ capersjones-maintenance.pdf 19
20. leading excellent quality control very good code structure at the initial release zero error-prone modules very low bad-fix injection rate of 1% or less result: maintenance costs can actually decline over the five year ownership period 20
21. average marginal quality control reasonable initial code structure one or two error-prone modules an average bad-fix injection rate of about 7% result: maintenance costs increase over a five-year period, but not at a very significant annual rate 21
22. lagging inadequate quality control poor code structure up to a dozen severe error-prone modules significant bad-fix injection rates of about 20% result: maintenance costs become more expensive every year due to entropy and an application that never stabilises 22
23. maintenance conclusion in the lagging scenario, ROI is negative in 3-5 years meaning the system needs to be replaced; this is tech bankruptcy leading scenario can have 20 - 30 years of positive ROI 23
24. an equation that can quantify complexity and debt complexity of a system which equation is the right one? acceleration of effort due to technical debt we’ll be looking for the effort to add one unit of complexity 24
25. complexity of a system 25
26. options for complexity measures intuition: complexity of adding a line of code based on how many other lines of code affected 26
27. options for complexity measures example write tests write line of code change all other places (code or tests) that depend on or are affected by repeat until finished 27
28. options for complexity measures we are looking for the ideal (best case) scenario n will be the amount of complexity 28
29. http://science.slc.edu/~jmarshall/courses/2002/spring/cs50/BigO/ 29
30. metcalfe’s law - a stretch value of a nextwork increases by the number of nodes because they are connected more or less n ^ 2 how does this apply? imagine building every connection, not just using it "Metcalfe-Network-Effect" by Woody993 at en.wikipedia - Transferred from en.wikipedia. Licensed under CC0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Metcalfe-Network-Effect.svg#mediaviewer/File:Metcalfe-Network-Effect.svg 30
31. binary trees - another stretch well known properties of algorithms (searching for example) number of connections between nodes increases as log n http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly 31
32. huge amount of work in software has been done to compartmentalise to avoid systemic effects object oriented programming patterns functions/procedures reusable code network layers interfaces protocols other domains methodologies for organisation change vast literature on how to run companies more effectively 32
33. 33 All of this allows for building larger and more complex systems • lines of code • number of people
34. base (best case) complexity good log(n) by decoupling and compartmentalising we reduce connections when a line of code is inserted, it only affects a small part of the network 34
35. Text log n http://stackoverflow.com/questions/ 2307283/what-does-olog-n-mean- exactly 35
36. Text 36
37. technical debt 37
38. technical debt in complexity terms tech debt takes log(n) of good code and makes it worse 38
39. does it accumulate quickly? should: see problem, then act problems it’s too late to brush and floss when the teeth are falling out tech debt accumulates silently, in multiple ways, the slowdown that results is gradual and then accelerates 39
40. does it accumulate quickly? decisions at multiple levels multiplier effect causing small concessions to add up quickly example management defers needed scope to gain speed team cuts corners to hit deadlines 40
41. multiplier effect management cuts scope team cut corners resulting debt 10% (90% done right) 10% (90% done right) 19% (0.9 * 0.9 = 0.81) 20% 20% 36% 30% 30% 51% 50% 50% 75% 41
42. complexity log(n) 42
43. debt: goodness 1 - debt 43
44. debt: exponent 44 1 1 - debt
45. together: complexity and debt log(n) 1 1 - debt 45
46. together: complexity and debt 46 log(n) 1 1 - debt
49. next steps for the theory prove equations are right better ways to measure debt and complexity 49
50. what can you do? if you aren’t scared yet… otherwise… symptoms: are you leading, average or lagging? differences are easily visible debt ratio needs to be low (but no need to know it exactly) do things properly i.e. peer reviews, formal controls, good structure 50
51. references Organisational change http://www.bristol.ac.uk/media-library/sites/cubec/migrated/documents/pr1.pdf Groups and scale http://www.shirky.com/writings/group_enemy.html Big O examples http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly Godel, Escher, Bach; I Am A Strange Loop; Douglas Hofstadter Capers Jones; Software Productivity Research Institute Quality excellence has ROI > $15 for each $1 spent; SOFTWARE QUALITY IN 2012:A SURVEY OF THE STATE OF THE ART http:// sqgne.org/presentations/2012-13/Jones-Sep-2012.pdf http://www.compaid.com/caiinternet/ezine/capersjones-maintenance.pdf http://insights.cermacademy.com/2012/05/preventing-software-failure-capers-jones-technologyrisk/ How To Measure Anything; Douglas Hubbard "Metcalfe-Network-Effect" by Woody993 at en.wikipedia - Transferred from en.wikipedia. Licensed under CC0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Metcalfe-Network-Effect.svg#mediaviewer/File:Metcalfe-Network-Effect.svg http://science.slc.edu/~jmarshall/courses/2002/spring/cs50/BigO/ http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly http://stackoverflow.com/questions/2307283/what-does-olog-n-mean-exactly Dunbar number http://en.wikipedia.org/wiki/Dunbar%27s_number Estimates of projects http://www.isbsg.org/ 51
52. references Your Code As a Crime Scene: Use Forensic Techniques to Arrest Defects, Bottlenecks, and Bad Design in Your Programs by Adam Tornhill https://pragprog.com/book/atcrime/your-code-as-a-crime-scene 52
53. questions twitter: @lukasco email: lukas.oberhuber@simplybusiness.co.uk linkdedin: uk.linkedin.com/in/lukasoberhuber 53