BestFor Incorporated is a rapidly growing online financial services company that offers guidance to its customers on a range of financial products. Its first website, BestForMortgages, was launched two years ago, shortly followed by BestForPensions. The popularity of both sites has driven the company to expand its offerings into other sectors and a third website, BestForFood, has recently been released.
The first two websites have proven to be functionally robust but suffered from performance issues due to:
Underestimation of concurrent user access at peak times in its original operational profiles
Data volumes rapidly growing beyond expectations on a single database shared by both sites.
All three websites share the same web server.
The BestForFood system has been running live for several months, however the following significant non-functional defects have been recorded:
A) The layout and navigation of the BestForFood website was not considered by the users as consistent with BestForPensions and BestForMortgages
B) Soon after delivering the BestForFood system the performance of both BestForPensions and BestForMortgages noticeably slowed
C) The time taken to diagnose, repair and re-test production defects did not meet BestForFood service level agreements
D) BestForFood performance was adversely impacted by unexpected growth in data volumes
E) The time taken to install monthly builds into the production system exceeded agreed timescales.
Which TWO of these defects might have been prevented had portability testing and compatibility testing been employed?
Stress testing is the most appropriate method for identifying weak areas in code that may cause performance issues, especially under conditions of extreme load or stress. This type of testing deliberately pushes the system beyond its normal operational capacity to see how it behaves under stress, which can help reveal vulnerabilities in error recovery and transactional integrity that are not apparent under normal conditions. Stress testing can provide early insights into potential failure points and help prioritize areas for improvement before full-scale deployment.
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