Software developers need to cope with complexity of Application Programming Interfaces (APIs) of external libraries or frameworks. However, typical APIs provide several thousands of methods to their client programs, and such large APIs are difficult to learn and use. An API method is generally used within client programs along with other methods of the API of interest. Despite this, co-usage relationships between API methods are often not documented. We propose a technique for mining Multi-Level API Usage Patterns (MLUP) to exhibit the co-usage relationships between methods of the API of interest across interfering usage scenarios. We detect multi-level usage patterns as distinct groups of API methods, where each group is uniformly used across variable client programs, independently of usage contexts. We evaluated our technique through the usage of four APIs having up to 22 client programs per API. For all the studied APIs, our technique was able to detect usage patterns that are, almost all, highly consistent and highly cohesive across a considerable variability of client programs.
Abstract-Interactive software visualization offers a promising support for program comprehension, including program dynamicity. We present, the extension of an existing visualization tool with heat maps to explore the time and other dimensions of software. To this end, we first propose a framework to unify the two main software dynamicities, execution and evolution. Then, this unified framework is exploited to define a visualization environment based on heat maps. We illustrate our approach on two comprehension tasks: understanding the behavior of programmers during the evolution of an application and understanding class contributions in use cases. The case studies show that the heat-map metaphor contributes to answer, more easily, many of the questions important to program comprehension.
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