We present a simple SSA construction algorithm, which allows direct translation from an abstract syntax tree or bytecode into an SSA-based intermediate representation. The algorithm requires no prior analysis and ensures that even during construction the intermediate representation is in SSA form. This allows the application of SSA-based optimizations during construction. After completion, the intermediate representation is in minimal and pruned SSA form. In spite of its simplicity, the runtime of our algorithm is on par with Cytron et al.’s algorithm. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Dynamic specification mining observes program executions to infer models of normal program behavior. What makes us believe that we have seen sufficiently many executions? The TAUTOKO (“Tautoko” is the Mãori word for “enhance, enrich.”) typestate miner generates test cases that cover previously unobserved behavior, systematically extending the execution space, and enriching the specification. To our knowledge, this is the first combination of systematic test case generation and typestate mining-a combination with clear benefits: On a sample of 800 defects seeded into six Java subjects, a static typestate verifier fed with enriched models would report significantly more true positives and significantly fewer false positives than the initial models.

Dynamic specification mining observes program executions to infer models of normal program behavior. What makes us believe that we have seen sufficiently many executions? The TAUTOKO 1 typestate miner generates test cases that cover previously unobserved behavior, systematically extending the execution space and enriching the specification. To our knowledge, this is the first combination of systematic test case generation and specification mining–a combination with clear benefits: On a sample of 800 defects seeded into six Java subjects, a static typestate verifier fed with enriched models would report significantly more true positives, and significantly less false positives than the initial models.