1. What are the contributions in "Generating java unit tests with ai planning" ?
In this paper, the authors compared the ability of three general-purpose AI planners to generate unit tests for Java classes with varying sizes and semantics.
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Generating Java unit tests with AI planning
Eddie Dingels,Timothy Fraser,Alexander J. Quinn +2 more
- 05 Nov 2007
- pp 2-6
TL;DR: This experiment compares the ability of three general-purpose AI planners to generate unit tests for Java classes with varying sizes and semantics and suggests a number of alternate search-based approaches.
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Abstract: Although several efforts have applied search-based approaches to the generation of unit tests, none have demonstrated that these approaches scale beyond small programs. Our experiment compares the ability of three general-purpose AI planners to generate unit tests for Java classes with varying sizes and semantics. Although the GraphPlan planner handled most of our test classes, none of our planners could handle them all. We suggest a number of alternate search-based approaches and conclude that further experiments are needed to evaluate them.
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Figures
Figure 1: A: fragment of class under test, B: fragment of unit test, C: experimental framework. 
Table 2: Statistics on problems with randomized goals after 20 trials. 
Table 1: Elapsedwall-clock time for planning. 0ms indicates elapsed time less than 1ms. FwdSch entries are the average of 20 actual runs.
Citations
AIana: an AI planning system for test data generation
Stefan Galler,Christoph Zehentner,Franz Wotawa +2 more
- 21 Jun 2010
TL;DR: This paper presents AIana, an approach for automatically generating complex objects used as test input data that satisfy a given precondition in terms of Design by Contract#8482; specification.
9
References
Fast planning through planning graph analysis
Avrim Blum,Merrick L. Furst +1 more
TL;DR: Graphplan as mentioned in this paper is a partial-order planner based on constructing and analyzing a compact structure called a planning graph, which can be used to find the shortest possible partial order plan or state that no valid plan exists.
1.6K
Feedback-Directed Random Test Generation
Carlos Pacheco,Shuvendu K. Lahiri,Michael D. Ernst,Thomas Ball +3 more
- 24 May 2007
TL;DR: Experimental results indicate that feedback-directed random test generation can outperform systematic and undirectedrandom test generation, in terms of coverage and error detection.
Using temporal logics to express search control knowledge for planning
Fahiem Bacchus,Froduald Kabanza +1 more
TL;DR: This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner.
730
Automated program flaw finding using simulated annealing
Nigel Tracey,John A. Clark,Keith C. Mander +2 more
- 01 Mar 1998
TL;DR: This paper outlines a generalised test-case data generation framework based on optimisation techniques that can incorporate a number of testing criteria, for both functional and non-functional properties.
226
Hierarchical GUI test case generation using automated planning
Atif M. Memon,Martha E. Pollack,Mary Lou Soffa +2 more
- 01 Feb 2001
TL;DR: A new technique to automatically generate test cases for GUIs that exploits planning, a well-developed and used technique in artificial intelligence, is presented and a prototype implementation of PATHS is described.