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.

Choices, choices: comparing between CHOC'LATE and the classification-tree methodology

Two popular specification-based test case generation methods are the choice relation framework and the classification-tree methodology. Both of them come with associated tools and have been used in different applications with success. Since both methods are based on the idea of partition testing, they are similar in many aspects. Because of their similarities, software testers often find it difficult to decide which method to be used in a given testing scenario. This paper aims to provide a solution by first contrasting the strengths and weaknesses of both methods, followed by suggesting practical selection guidelines to cater for different testing scenarios