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Software process measurement and related challenges in agile software development a multiple case study

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Software process measurement and related challenges in agile software development - a multiple case study

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Software process measurement and related challenges in agile software development a multiple case study

  1. 1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732253. Software Process Measurement and Related Challenges in Agile Software Development – A Multiple Case Study Prabhat Ram Pilar Rodriguez Markku Oivo M3S, University of Oulu
  2. 2. Q-Rapids – In Brief Agile-based, data-driven, quality-aware framework Objectives: oImprove quality oIncrease productivity oReduce time to market
  3. 3. Research Problem Planning & Monitoring Understanding Quality Improve Sw. Development Academic Literature, Emerging results Industrial Context Decision-making Metrics use Operationalization Challenges Better Measurement Goals Operational benefits Operationalization Challenges Process metrics Process Metrics for ASD and rationale Operationalization Challenges RQ1 RQ2
  4. 4. Research Context Company & Size Product Development Method Development Team A – Medium Production testing framework Scrum 1 team (6-7 members) B – Large Software platform Scrum 8 globally distributed teams C – Large Modeling tool Ad-hoc process following Agile principles 1 team (9 members) D - Small Not disclosed ScrumBan & ScrumBut 10 members (same core team)
  5. 5. Data Collection QuaMoCo, GQM & GQM+ Strategies workshops Product Process Product Process …. Product Product Product Product …. Process Process …. Process Process Process Process …. GQM … Strategic Indicator Quality Factors Quality Factors Assessed Metric 1 Assessed Metric 2 Assessed Metric 3 Assessed Metric 4 Raw Metric 1 Raw Metric 2 Raw Metric 3 Raw Metric 4 Raw Metric 5 Quality Model Data Collection
  6. 6. GQM Workshops Company Parameters Session 1 Session 2 Session 3 Company A No. Of participants 4 4 4 Role (# participants) Quality Lead, Developers (2), Requirement & Process Lead Quality Lead, Developers (2), Requirement & Process Lead Quality Lead, Developers (2), Requirement & Process Lead Length (hours) 3.5 2.5 2.5 Recorded/Documented Documented Both Both Company B No. Of participants 8 5 4 Role (# participants) Quality Manager (2), Project Manager, Developer (3) Development Manager (2) Quality Manager, Project Manager, Developer (2) Development Manager Quality Manager, Project Manager, Developer (2) Length (hours) 3.5 2 1 Recorded/Documented Both Both Both Company C No. Of participants 4 2 1 Department (# participants) Architect/Developer, Project Manager, R&D Manager, CEO/Product Owner Architect/Developer and R&D Manager Architect/Developer Length (hours) 3 1 0.5 Recorded/Documented Both Both Documented Company D No. Of participants 3 2 1 Department (# participants) Product Owner, Project Manager, System Designer Product Owner, Project Manager Project Manager Length (hours) 3 1.5 1.5 Recorded/Documented Documented Documented Documented
  7. 7. Result – Choice of Process Metrics 132 Metrics Testing Performance Issues’ Velocity Code Quality Issues’ Estimation Accuracy Testing Status Blocking Code Delivery Performance External Quality Development Speed Quality Issues Specitications Mostly custom metrics preferred All Use Cases Company A, B & D Company B & C Company C Company A Company B
  8. 8. Result – Rationale System provides a lot of rich data worth exploring ; Tool produces lot of data that company is interested in Review phase iterations is a bottleck at the moment ; ”This could show potential testing bottlenecks…” ”Are we bad at planning?” “Because it is in…in some tool it is user story, in some tool it is issue, and some tool it is fault, and some tool it is something else, and they don't have the same ID to connect those.” “One of failures…we see that...Okay, there was this kind of root cause for that…so, can we spread this information with everybody, so that not eight people are doing the same mistake in the next project“ Data Availability Planning and bottleneck tracking Information Consistency Visibility Data Capitalization • Awareness • Control
  9. 9. Result – Operationalization Challenges "But is there any kind of…this kind of data available…not. “ ; "You have no way of measuring how long that reviewer went through the code." ; "We don’t know what information in this specific tool is available." "That’s a process change. We should then change our process” ; “We are really interested in this, but...for some reason, the company has made this kind of a decision, that this is not…not follow this.." “…It should be adding value. What we need to do is…we need to clarify a number of these metrics, and how we are going to use that kind of, let's say work, on the software that we are working on." ; “…we have plenty of data and tools to collect metrics, but we have shortcomings for efficiently and smartly utilizing the collected data” Lack of data Process restrictions Uncertain potential for actionable inputs Data Unavailability • Process Inertia Lack of Actionable Input
  10. 10. Conclusion Choice of Process Metrics • Plan and track sprint and projects • Compliance to Agile principles (shorter cycles, good design, technical excellence) • Context influences (company size, project characteristics) Rationale • Data Capitalization - Improvement through awareness and control o ”Control” in contrast to Agile tenets, but necessary Operationalization Challenges • Data unavailability o Incompatibility between metrics and process • Skeptical about metrics facilitating action • Difficult to extract actionable intelligence
  11. 11. Thank you

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