Testing Container Classes: Random or Systematic?

Container classes such as lists, sets, or maps are elementary data struc-tures common to many programming languages. Since they are a part of stan-dard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these techniques have not been thor-oughly compared to simpler techniques such as random testing. We present the results of a larger case study in which we compare random testing with shape ab-straction, a systematic technique that showed the best results in a previous study. Our experiments show that random testing is about as effective as shape abstrac-tion for testing these containers, which raises the question whether containers are well suited as a benchmark for comparing advanced testing techniques

On Parameter Tuning in Search Based Software Engineering

Abstract. When applying search-based software engineering (SBSE) techniques one is confronted with a multitude of different parameters that need to be chosen: Which population size for a genetic algorithm? Which selection mechanism to use? What settings to use for dozens of other parameters? This problem not only troubles users who want to apply SBSE tools in practice, but also researchers performing experimentation – how to compare algorithms that can have different parameter settings? To shed light on the problem of parameters, we performed the largest empirical analysis on parameter tuning in SBSE to date, collecting and statistically analysing data from more than a million experiments. As case study, we chose test data generation, one of the most popular problems in SBSE. Our data confirm that tuning does have a critical impact on algorithmic perfor-mance, and over-fitting of parameter tuning is a dire threat to external validity of empirical analyses in SBSE. Based on this large empirical evidence, we give guidelines on how to handle parameter tuning

Adaptive Evolutionary Testing: an Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

Adaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm’s efficiency considerably, while introducing a negligible computational overhead

Análise de sensibilidade da margem bruta da receita e dos custos do confinamento de diferentes grupos genéticos Gross margin sensitivity analyses of the income and costs of feedlot for different genetic groups

Análises de sensibilidade da margem bruta foram realizadas com dados de desempenho e custos de 149 animais confinados por períodos variáveis de acordo com acabamento de carcaça dos grupos genéticos: Nelore sobreano, Nelore desmamado, ½ Valdostana ½ Nelore, ½ Simental ½ Nelore, ½ Braford ½ Brangus, ½ Braford ¼ Angus ¼ Nelore, Brangus, ½ Canchim ¼ Angus ¼ Nelore e ½ Canchim ¼ Simental ¼ Nelore. Sensibilidade da margem bruta foi realizada com variações nos preços de venda da carne e de compra do milho, soja, silagem de milho e boi magro, mantendo-se os demais preços fixos. Com o preço da carne a 0,99 US$/kg, o Nelore sobreano foi mais eficiente nas variações do milho, soja e silagem de milho; Brangus foi mais eficiente nas variações do preço de compra do boi. Com preço da carne a 1,27 US$/kg, a margem bruta foi positiva para todos grupos genéticos nas variações do milho, soja e silagem de milho, exceto Nelore desmamado. Na variação de preços de compra do boi, apenas Brangus apresentou margem bruta positiva. A margem bruta foi mais sensível às variações no preço de compra dos animais do que no preço de venda da carne.<br>Gross margin sensitivity analyses based on costs and feedlot performances of 149 steers of different genetic groups were performed. The genetic groups consisted of yearling Nellore, weaned Nellore, ½ Valdostana ½ Nellore, ½ Simmental ½ Nellore, ½ Braford ½ Brangus,½ Braford ¼ Angus ¼ Nellore, Brangus, ½ Canchim ¼ Angus ¼ Nellore and ½ Canchim ¼ Simmental ¼ Nellore. The gross margin sensitivity analyses were performed considering fluctuation of beef selling price, and buying price fluctuations of corn, soybean, corn silage and steers, maintaining all the remained costs fixed. For beef selling price of .99 US$/kg yearling Nellore was more efficient for fluctuation prices of corn and soybean, corn silage and the genetic group Brangus was more efficient for fluctuation of beef buying prices. For beef selling price of 1.27 US$/kg, the gross margin was positive for all genetic group, except the weaned Nellore for corn, soybean and corn silage buying price fluctuations; Brangus group showed positive gross margin for all animal buying price fluctuations. The animal gross margin showed more sensitivity to the buying price fluctuations in comparison to the animal selling price fluctuations

Enabling Object Reuse on Genetic Programming-Based Approaches to Object-Oriented Evolutionary Testing

Recent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data. However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process