Excellent work (-) has shown that memory management and transaction concurrency levels can often be tuned automatically by the database management systems. Other excellent work (]-) has shown how to use the optimizer to do automatic physical design or to make the optimizer itself more self-adaptive (-). Our performance tuning experience across various industries (finance, gaming, data warehouses, and travel) has shown that enormous additional tuning benefits (sometimes amounting to orders of magnitude) can come from reengineering application code and table design. The question is: can a tool help in this effort? We believe so. We present a tool called AppSleuth that parses application code and the tracing log for two popular database management systems in order to lead a competent tuner to the hot spots in an application. This paper discusses (i) representative application "delinquent design patterns", (ii) an application code parser to find them, (iii) a log parser to identify the patterns that are critical, and (iv) a display to give a global view of the issue. We present an extended sanitized case study from a real travel application to show the results of the tool at different stages of a tuning engagement, yielding a 300 fold improvement. This is the first tool of its kind that we know of.