Version 17.1 by eMagiz on 2022/06/10 10:42

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2 In this microlearning, we will take a look at peer reviews for eMagiz.
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4 Should you have any questions, please contact [[academy@emagiz.com>>mailto:academy@emagiz.com]].
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6 * Last update: April 22nd, 2021
7 * Required reading time: 8 minutes
8
9 == 1. Prerequisites ==
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11 * Basic knowledge of the eMagiz platform
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13 == 2. Key concepts ==
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15 Peer reviews are defined as follows: A disciplined engineering practice for detecting and correcting defects in software artifacts and preventing their leakage into production. Its a well known and working concept with IT organization, and it can definetely applied in DevOps teams that have eMagiz as one of the technology pillars. In tbe context of eMagiz, peer reviews are done usually after the Create phase.
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17 [[image:Main.Images.Microlearning.WebHome@intermediate-devops-perspectives-peerreview-1.png]]
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19 Key benefits of peer reviews
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21 * Improved quality of integrations
22 * Higher consistency
23 * Knowledge sharing
24 * Keeping standards for optimal maintenance
25 * Architecture challenge and verification
26 * Find alternative solutions
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28
29
30 == 3. Running peer reviews in eMagiz ==
31
32 === 3.1 Considerations for reviewee ===
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34 Here are some things to keep in mind when presenting the work to peer review.
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36 * Quickly explain the story / task / background
37 * Quickly show the working result if applicable / practical
38 * Talk through the solution while showing the models / code
39 * Just trying to explain your work to someone else will help spot mistakes
40 * Don’t show every single detail but try to highlight the important parts and/or details you’re less sure about. This takes time and experience to get “right” and is different depending on the story, the reviewee, the reviewer, the project, etc.
41 * Always do a peer review, no exceptions. Making assumptions about the usefulness beforehand defeats the whole purpose.
42
43 === 3.2 Considerations for reviewer ===
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45 Here are some things to keep in mind when peer reviewing the work .
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47 * Ask questions
48 * How does this work?
49 * Why did you decide to …?
50 * Did you think about …?
51 * Spot (incorrect) assumptions
52 * Check application of best practices – see next slide
53 * Modelling / coding patterns
54 * Naming conventions
55 * Errors / warnings
56 * Notice non-standard / unusual / abnormal things
57 * Make sure this is documented, mainly for future changes. Annotations are very useful here.
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59 === 3.3 Peer review items per ILM Phase ===
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61 * Capture
62 * 100% filled
63 * Connection method clear
64 * Authentication method clear
65 * Definitions loaded
66 * Sizing impact understood and valid
67 * Design
68 * Check solution architecture validity
69 * Design 100% filled and clear
70 * CDM Root entity mapped
71 * Set as mapped – avoid line mapping
72 * Use annotation where possible
73 * Proper flow and system settings
74 * Create
75 * Validate routing
76 * Generic error response flows
77 * Check naming conventions flows, properties and XSD
78 * Split messages in on-ramp – not later
79 * Deploy
80 * Check properties
81 * Avoid too many different flow versions – max. 2
82 * Remove test packages that are deployed
83 * Manage
84 * All alerts mapped to Customer Support
85 * All messages can be explained
86 * Avoid code mappings
87 * Enable default alerts
88 * Architecture
89 * Deploy connector close to the source/target system
90 * Ensure ACCP and PROD are exact copies
91 * Cloud over on-premise
92 * No hard-coded variable – use properties
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94
95
96 == 4. Assignment ==
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98 See how peer reviews are currently implemented within the projects on which you work to see if you can learn something from the information you have gathered via this microlearning.
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100 == 5. Key takeaways ==
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102 Peer reviews are instrumental in any DevOps team. Use the provided list as your team's peer review starting point and tune as you go along.
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104
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106 == 6. Suggested Additional Readings ==
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108 You will find plenty background items available on the Internet.
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110 == 7. Silent demonstration video ==
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112 As this is a more theoretical microlearning we have no video for this.
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