Track: Test Strategy, Process and Design
Currently in many organizations, especially ours, we continue to see “quality” viewed as functional testing only. There is not much visibility into the quality of work products in the earlier phases of the SDLC which then leads itself to higher SDLC cost of quality. What needs to be done is a complete mind shift in which everyone on the team begins to realize that they are responsible for quality. Everyone!!! Once that begins to be understood it becomes more evident that people begin thinking about how their work will impact others as well as how other people’s work will impact theirs. The silos begin to be broken down. Once this concept is adopted it lends itself to greater synergy throughout the group. Then as a byproduct people have a much clearer purpose for the work they do including passion for their jobs.
At the Hartford, we were trying to solve for the persistent issues of unpredictable quality and schedule, high cost of SDLC quality and low credibility with our business customers. In order to solve our problem, we assessed all of the great things we were doing in the functional QA space to see where we could “shift left”. This shift is a transformation strategy to help build quality across the SDLC phases. We looked at where many of our quality gaps appeared to be deriving from. What we were able to quickly determine was that there was little to no traceability, very little to no testing being done in the earlier phases such as Unit and Integration/Assembly tests. We also didn’t have any metrics early on to gauge where we were from a quality perspective. We were able to quickly put processes and procedures into working order to get this moving in the right direction. We embarked on an advanced quality planning exercise at the beginning of the pilot project involving all SDLC stakeholders to define how quality should be built and controlled across the SDLC. The key control checkpoint introduced was the end to end bi-directional traceability of requirements to specifications to design to unit tests, integration tests, system tests and defects. These translated into a Quality Performance Index model with checklists for each work product by SDLC phase to measure and control quality. Key quality metrics were implemented to help measure performance. These include SDLC cost of quality, defect density, automated code scans, cyclomatic complexity and requirements stability by effort. Results: E2E (SDLC) Quality Management function has brought greater visibility and transparency into the quality of work products from Requirements, Design and Build phases through the Quality Performance Index model. The major benefits achieved are a significant reduction in SDLC defect density (measured in terms of defects to actual efforts), 95% test case pass rate from day 1 through end of system testing, and reduction in test cycles. The business value realized from the first pilot work is ~10% reduction in the SDLC cost of quality that helped reduce the overall project efforts and helped deliver it on time. Overall, predictability in quality, cost and schedule is achieved, as well as higher levels of satisfaction of the work products being delivered and received.
Session Takeaways Include Strategies On How To:
- Provide predictable software quality.
- Prevent early defects in the SDLC phases.
- Reduce the overall cost of quality.
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Autumn Norris – IT Director, The Hartford
Autumn Norris is a Director of IT Quality at The Hartford. She has over 10 years of quality experience across various roles. Autumn has significant experience managing distributed and offshore test teams. In an effort to provide predictable software quality, as well as reducing the overall “Cost of Quality” and providing improved software reliability, she is currently leading the effort to rollout SDLC Quality Management across the enterprise. Autumn has been working with her peers to build quality into the earlier phases of the SDLC by utilizing “Shift Left” techniques, enhanced quality governance and implementing optimized test metrics.