Discovering Math APIs by Mining Unit Tests

Abstract. Intoday’sAPI-richworld,programmerproductivitydepends heavily on the programmer’s ability todiscover the requiredAPIs. In this paper, we present a technique and tool, called MathFinder, to discover APIs for mathematical computations by mining unit tests of API methods. Given a math expression, MathFinder synthesizes pseudo-code to compute the expression by mapping its subexpressions to API method calls. For each subexpression, MathFinder searches for a method such that there is a mapping between method inputs and variables of the subexpression. The subexpression, when evaluated on the test inputs of the method under this mapping, should produce results that match the method output on a large number of tests. We implemented Math-Finder as an Eclipse plugin for discovery of third-party Java APIs and performed a user study to evaluate its effectiveness. In the study, the use of MathFinder resulted in a 2x improvement in programmer productivity. In 96 % of the subexpressions queried for in the study, Math-Finder retrieved the desired API methods as the top-most result. The top-most pseudo-code snippet to implement the entire expression was correct in 93 % of the cases. Since the number of methods and unit tests to mine could be large in practice, we also implement MathFinder in a MapReduce framework and evaluate its scalability and response time.

Foraging and navigations, fundamentally: Developers\u27 predictions of value and cost

Empirical studies have revealed that software developers spend 35%-50% of their time navigating through source code during development activities, yet fundamental questions remain: Are these percentages too high, or simply inherent in the nature of software development? Are there factors that somehow determine a lower bound on how effectively developers can navigate a given information space? Answering questions like these requires a theory that captures the core of developers\u27 navigation decisions. Therefore, we use the central proposition of Information Foraging Theory to investigate developers\u27 ability to predict the value and cost of their navigation decisions. Our results showed that over 50% of developers\u27 navigation choices produced less value than they had predicted and nearly 40% cost more than they had predicted. We used those results to guide a literature analysis, to investigate the extent to which these challenges are met by current research efforts, revealing a new area of inquiry with a rich and crosscutting set of research challenges and open problems