Learning how to search: generating effective test cases through adaptive fitness function selection

Search-based test generation is guided by feedback from one or more fitness functions—scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage—do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite unit test generation framework, and used these algorithms to dynamically set the fitness functions used during generation for the three goals identified above. We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples. EvoSuiteFIT techniques attain significant improvements for two of the three goals, and show limited improvements on the third when the number of generations of evolution is fixed. Additionally, for two of the three goals, EvoSuiteFIT detects faults missed by the other techniques. The ability to adjust fitness functions allows strategic choices that efficiently produce more effective test suites, and examining these choices offers insight into how to attain our testing goals. We find that adaptive fitness function selection is a powerful technique to apply when an effective fitness function does not already exist for achieving a testing goal.

High Intensity Functional Training (HIFT) Improves Fitness in Recruit Firefighters

Background: Firefighting is a strenuous profession requiring adequate levels of fitness for effective job performance. Providing firefighters with a safe and effective fitness program is essential for optimal performance. The purpose of this project was to examine changes in various parameters of physical fitness and firefighter ability following a 7-week high intensity functional training (HIFT) program. Methods: Participants were male firefighter recruits (N = 89; age = 27.1 ± 4.2 years, height = 1.78 ± 0.1 m, BMI = 28.1 ± 4.2) enrolled in a Basic Operations Firefighter Academy. Fitness and firefighting ability (via the Academy Firefighter Challenge) were assessed at Weeks 1 and 7 of the Academy. Results: Significant improvements in both fitness and firefighter ability were seen following the HIFT program. Specifically, fitness (BMI, cardiovascular fitness, muscular endurance) improved significantly [Hotelling’s T2 = 8.98, F(5, 84) = 150.92, p < 0.001, η2p = 0.90]. Firefighter ability also improved significantly [Hotelling’s T2 = 3.95, F(7, 88) = 46.26, p < 0.001, η2p = 0.80]. Conclusions: Following a 7-week Basic Operations Firefighter Academy that included daily HIFT, significant increases in fitness and firefighting ability were observed. These findings suggest that HIFT appears to be an effective means of improving fitness and firefighting ability in recruit firefighters.

General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part II

This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.

A Study on Deriving a Method for Chromosome Similarities Suitable for the Search Space

Evolutionary Computations (ECs) are powerful search algorithms for nonlinear problems, and have been. widely studied. Generally, calculation of EC to acquire expected solutions takes much time because they need repeated calculation for search solutions. Hanaki et al. proposed the fitness inference to reduce evaluation time by simplifying calculation of the fitness of chromosomes. In fitness inference, how chromosome similarities is defined is very important corresponding to that of solutions. We studied chromosome similarities considering the solution similarities, and propose new chromosome similarities with the weight on each locus determined by the change of information on the locus using Sequential Difference Fitness Value Allocation. We used benchmark functions to study the feasibility of the proposed method and found that effective weights on loci suitable for the search space are automatically generated, and that the proposed method enables effective fitness inference.