Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.     

The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process

Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes. Recent empirical studies have used open source systems. These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects. Whether software metrics can still predict class error proneness in a system’s post-release evolution is still a question to be answered. This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release. Furthermore, we found that the prediction cannot be used to build a metrics model to identify error-prone classes with acceptable accuracy. These findings suggest that as a system evolves, the use of some commonly used metrics to identify which classes are more prone to errors becomes increasingly difficult and we should seek alternative methods (to the metric-prediction models) to locate error-prone classes if we want high accuracy.     

Analyzing clusters of class characteristics in OO applications

The transition from Java 1.4 to Java 1.5 has provided the programmer with more flexibility due to the inclusion of several new language constructs, such as parameterized types. This transition is expected to increase the number of class clusters exhibiting different combinations of class characteristics. In this paper we investigate how the number and distribution of clusters are expected to change during this transition. We present the results of an empirical study were we analyzed applications written in both Java 1.4 and 1.5. In addition, we show how the variability of the combinations of class characteristics may affect the testing of class members.     

Segmented software cost estimation models based on fuzzy clustering

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees.This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.     

Examining the significance of high-level programming features in source code author classification

The use of Source Code Author Profiles (SCAP) represents a new, highly accurate approach to source code authorship identification that is, unlike previous methods, language independent. While accuracy is clearly a crucial requirement of any author identification method, in cases of litigation regarding authorship, plagiarism, and so on, there is also a need to know why it is claimed that a piece of code is written by a particular author. What is it about that piece of code that suggests a particular author? What features in the code make one author more likely than another? In this study, we describe a means of identifying the high-level features that contribute to source code authorship identification using as a tool the SCAP method. A variety of features are considered for Java and Common Lisp and the importance of each feature in determining authorship is measured through a sequence of experiments in which we remove one feature at a time. The results show that, for these programs, comments, layout features and package-related naming influence classification accuracy whereas user-defined naming, an obvious programmer related feature, does not appear to influence accuracy. A comparison is also made between the relative feature contributions in programs written in the two languages.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Trust in consumer-to-consumer electronic commerce

We developed a model of consumer-to-consumer (C2C) e-commerce trust and tested it. We expected that two influences: internal (natural propensity to trust [NPT] and perception of web site quality [PWSQ]) and external (other's trust of buyers/sellers [OTBS] and third party recognition [TPR]) would affect an individual's trust in C2C e-commerce. However contrary to studies of other types of e-commerce, support was only found for PWSQ and TPR; we therefore discussed possible reasons for this contradiction. Suggestions are made of ways to help e-commerce site developers provide a trustworthy atmosphere and identify trustworthy consumers.     

Performance analysis of an adaptive dynamic grid-based approach to data distribution management

Data distribution management (DDM) plays a key role in traffic control for large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the region-based, fixed grid-based, and dynamic grid-based (DGB) schemes, as well as grid-filtered region-based and agent-based DDM schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the adaptive dynamic grid-based (ADGB) scheme that optimizes DDM time through the analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the distribution rate (DR) is devised. The DR represents the relative processing load and communication load generated at each federate. The DR and the matching performance are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the DGB scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.     

An interference-aware fair scheduling for multicast in wireless mesh networks

Multicast is a fundamental routing service in wireless mesh networks (WMNs) due to its many potential applications such as video conferencing, online games, and webcast. Recently, researchers proposed using link-quality-based routing metrics for finding high-throughput paths for multicast routing. However, the performance of such link-quality-based multicast routing is still limited by severe unfairness. Two major artifacts that exist in WMNs are fading which leads to low quality links, and interference which leads to unfair channel allocation in the 802.11 MAC protocol. These artifacts cause the multicast application to behave unfairly with respect to the performance achieved by the multicast receivers.     

A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.