6263618
Evaluation of mutation testing for object-oriented programs
The effectiveness of mutation testing depends heavily on the types of faults that the mutation operators are designed to represent. Thus, the quality of the mutation operators is key to mutation testing. Although, mutation operators for object-oriented languages have previously been presented, little research has been done to show the usefulness of the class mutation operators. To assess the usefulness of class mutation operators, we conducted two empirical studies. In the first study, we examine the number and kinds of mutants that are generated for object-oriented programs. In the second study, we investigate the way in which class mutation operators model faults that are not detected by traditional mutation testing. We conducted our studies using a well-known object-oriented system, BCEL.
16141068
Improving cohesion metrics for classes by considering dependent instance variables
The existing cohesion metrics for classes do not consider the characteristics of dependent instance variables that are commonly used in a class and, thus, do not properly reflect the cohesiveness of the class. This paper presents an approach for improving the cohesion metrics by considering the characteristics of the dependent instance variables in an object-oriented program.
31388642
Modeling and Testing Agent Systems Based on Statecharts
This paper presents a testing method for an agent system against its specification described with Statecharts. Here, an agent system means an implementation of an agent application. Its testing method generates test sequences from the specification and checks whether the agent system behaves in accordance with the specification by executing the test sequences. For specification-based testing, we have extended Statecharts in order to describe the behavior of an agent system according to the concept of Agent UML. Then, we propose an approach to generating test sequences from the extended Statecharts. Our approach makes effective use of partial order methods for considering representative sequences of equivalent classes divided from all possible sequences. Therefore, we can efficiently manage a large number of possible sequences caused by the agents’ autonomy. As a result, we can reduce the number of test sequences and the number of executions to be tested.
15507852
Testing of concurrent programs based on message sequence charts
Most prior work on specification-based testing of concurrent programs have postulated an equivalence relation between specifications and programs. However the equivalence relation, which requires a specification and a program to encompass the same set of synchronization sequences, is not well-suited for resting concurrent programs against partial and nondeterministic specifications. We present a new specification-based approach to testing concurrent programs against Message Sequence Charts (MSCs) with partial and nondeterministic semantics. New conformance relations, named behavioral conformance and nondeterminacy conformance, are defined and the details of the proposed testing methodology based on the conformance relations are given. The effectiveness of our testing approach is investigated using a telephone switching system example.
12216769
Change impact analysis for a class hierarchy
Modified programs are required to be retested to determine whether changes have been made correctly and to investigate whether those cause any adverse effect on the program behavior. Then, the key issue is the extent of the retesting tasks. To save time and efforts, retesting should be done on only the parts that might be affected by changes. Change impact analysis is the activity which identifies the parts to be retested. Object-oriented paradigm is based on several concepts such as encapsulation, inheritance, polymorphism, and dynamic binding. Although these features contribute to the reusability and extensibility of systems, they produce complex dependencies between classes and attributes, which make it difficult to identify the affected components by changes. We propose an approach for analyzing change impact in a class hierarchy. Our approach is based on the class firewall method, but aims to reduce the retesting effort significantly by taking a member function as a test unit. We deal with the impacts of changes associated with various object-oriented features as well as types of changes which can occur at the level of a data member a member function, a class, and an inheritance relation.
9037240
Empirical evaluation of a fuzzy logic-based software quality prediction model
Software inspection, due to its repeated success on industrial applications, has now become an industry standard practice. Recently, researchers began analyzing inspection data to obtain insights on how software processes can be improved. For example, project managers need to identify potentially error-prone software components so that limited project resource may be optimally allocated. This paper proposes an automated and fuzzy logic-based approach to satisfy such a need. Fuzzy logic offers significant advantages over other approaches due to its ability to naturally represent qualitative aspect of inspection data and apply flexible inference rules. In order to empirically evaluate the effectiveness of our approach, we have analyzed published inspection data and the ones collected from two separate inspection experiments which we had conducted. χ2 analysis is applied to statistically demonstrate validity of the proposed quality prediction model.
18194762
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.
10182888
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure optimization in artificial neural networks. There were many nonlinear feature extraction methods using neural networks but they still have the same difficulties arisen from the fixed network topology. In this paper, we propose a novel combination of genetic algorithm and feedforward neural networks for nonlinear feature extraction. The genetic algorithm evolves the feature space by utilizing characteristics of hidden neurons. It improved remarkably the performance of neural networks on a number of real world regression and classification problems.
15346879
A 3-D FPGA wire resource prediction model validated using a 3-D placement and routing tool
The interconnection architecture of FPGAs such as switches dominates performance of FPGAs. Three-dimensional integration of FPGAs overcomes interconnect limitations by allowing instances to be located and signals to be routed in 3-D space. Wire resource prediction is important for fast and accurate interconnection planning in 3-D FPGA. In this paper, we extend the existing analytic model shown in [13] with a new parameter for our 3-D FPGA which has cluster-based logic blocks. The proposed wire resource prediction model is applied to our 3-D FPGA using a Xilinx Virtex2 slice [18] and our 3-D routing architecture. We validate the effectiveness of the extended model by comparing the required number of channel wires predicted by the extended analytic equation with that of the placed and routed results using 3-D placement and routing algorithm designed for our 3-D FPGA for a number of benchmark circuits. The extended 3-D wire resource prediction model predicts the required channel capacity with an average of 11.1% error for 17 large circuits from LGSynth93 and ISPD2001 Verilog benchmarks.
5100963
Scalable Modeling and Performance Evaluation of Wireless Sensor Networks
A notable features of many proposed Wireless Sensor Networks (WSNs) deployments is their scale: hundreds to thousands of nodes linked together. In such systems, modeling the state of the entire system as a cross-product of the states of individual nodes results in the well-known state explosion problem. Instead, we represent the system state by the probability distribution on the state of each node. In other words, the system state represents the probability that a randomly picked node is in a certain state. Although such statistical abstraction of the global state loses some information, it is nevertheless useful in determining many performance metrics of systems that exhibit Markov behavior. We have previously developed a method for specifying the performance metrics of such systems in a probabilistic temporal logic called iLTL and for evaluating the behavior through a novel method for model checking iLTL properties. In this paper, we describe a method for estimating the probabilities in a Discrete Time Markov Chain (DTMC) model of a large-scale system. We also provide a statistical test so that we can reject estimated DTMCs if the actual system does not have Markov behavior. We describe results of experiments applying our method toWSNs in an experimental test-bed, as well as using simulations. The results of our experiments suggest that our model estimation and model checking method provides a systematic, precise and easy way of evaluating performance metrics of some large-scale WSNs.
493776
Evolutionary Ensemble for Stock Prediction
We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable improvement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.