A General Model of Unit Testing Efficacy

Much of software engineering is targeted towards identifying and removing existing defects while preventing the injection of new ones. Defect management is therefore one important software development process whose principal aim is to ensure that the software produced reaches the required quality standard before it is shipped into the market place. In this paper, we report on the results of research conducted to develop a predictive model of the efficacy of one important defect management technique, that of unit testing. We have taken an empirical approach. We commence with a number of assumptions that led to a theoretical model which describes the relationship between effort expended and the number of defects remaining in a software code module tested (the latter measure being termed correctness). This model is general enough to capture the possibility that debugging of a software defect is not perfect and could lead to new defects being injected. The Model is examined empirically against actual data and validated as a good predictive model under specific conditions. The work has been done in such a way that models are derived not only for the case of overall correctness but also for specific types of correctness such as correctness arising from the removal of defects contributing to shortcoming in reliability (R-type), functionality (F-type), usability (U-type) and maintainability (M-type) aspects of the program subject to defect management.     

A dynamic priority approach to reducing delay in interactive TCP connections

Many interactive applications require continuous user interaction, for example ssh and many web applications like online games. The TCP connections created by these applications are in a class called interactive. When interactive applications suffer from packet loss, packet retransmission time severely increases the user perceived delay. This work introduces a Dynamic Priority RED Queue (DPRQ) algorithm that dynamically changes the priority of queues instead of dropping packets when the interactive queue is overloaded. The algorithm reduces the user perceived delay by reducing packet loss in interactive TCP connections. The DPRQ is compared to an existing Class-Based Queue which incorporates RED (RCBQ) as would typically be used for Assured Forwarding. An analytical model of the DPRQ and RCBQ is presented with both experimental and analytical results. The DPRQ is found to decrease packet loss by up to eight times and therefore provide a lower user perceived delay even though queuing delay is increased by up to five times.     

Task assignment with work-conserving migration

In this paper we a present a task assignment policy suited to environments (such as high-volume web serving clusters) where local centralised dispatchers are utilised to distribute tasks amongst back-end hosts offering mirrored services, with negligible cost work-conserving migration available between hosts. The TAPTF-WC (Task Assignment based on Prioritising Traffic Flows with Work-Conserving Migration) policy was specifically created to exploit such environments. As such, TAPTF-WC exhibits consistently good performance over a wide range of task distribution scenarios due to its flexible nature, spreading the work over multiple hosts when prudent, and separating short task flows from large task flows via the use of dual queues. Tasks are migrated in a work-conserving manner, reducing the penalty associated with task migration found in many existing policies such as TAGS and TAPTF which restart tasks upon migration. We find that the TAPTF-WC policy is well suited for load distribution under a wide range of different workloads in environments where task sizes are not known a priori and negligible cost work-conserving migration is available.     

Task assignment in multiple server farms using preemptive migration and flow control

Existing task assignment policies proposed for assigning tasks in stand-alone server farms are not efficient in multiple server farm environments because they have not been designed to exploit the properties of such environments. With the emergence of high speed networks and operating systems that have features such as preemptive migration, the importance of designing task assignment policies for assigning tasks in multiple server farms has increased. Such policies can result in better overall performance compared to those that optimise performance in stand-alone server farms.This paper proposes a task assignment policy suitable for assigning tasks in multiple server farms. The proposed policy, called Multi-Cluster Task Assignment based on Preemptive Migration (MCTPM) is based on a multi-tier host architecture that reduces the variance of task sizes in host queues by processing tasks with similar sizes using a set of hosts that have a distinct task size range. MCTPM controls the traffic flow into server farms via a global dispatching device so as to optimise the performance. MCTPM supports preemptive task migration between servers in the same farm and between servers in different farms.Performance analysis of the proposed policy indicates that significant performance improvements are possible under a wide range of workload scenarios. For example, MCTPM outperforms existing policies such as MC-Random, MC-TAGSPM and MC-MTTPM by factors of 190, 5 and 10.5 respectively under certain scenarios.     

Analysis of Rainfall Severity and Duration in Victoria,Australia using Non-parametric Copulas and Marginal Distributions

The analysis of joint probability distributions of rainfall characteristics such as severity and duration is important in water resources management. Deriving their distributions using standard statistical techniques are often problematical due to its complexity. Standard methods usually assume that the rainfall characteristics are independent or that their marginal distributions belong to the same family of distributions. The use of copulas based methodologies can circumvent these restrictions and are therefore increasingly popular. However, the copulas and marginal distributions that are commonly used belong to specific parametric families and their adoption could lead to spurious inferences if the underlying assumptions are violated. For this reason, we recommend a nonparametric or semiparametric approach to estimate the joint distribution of rainfall characteristics. In this paper, we introduce and compare several copula–based approaches, each involving a combination of parametric or nonparametric marginal distributions conjoined by a parametric or nonparametric copula. An empirical illustration of the different approaches using rainfall data collected from six stations in the state of Victoria, Australia, demonstrated that a nonparametric approach can often give better results than a purely parametric approach.     

Biohydrogen and biogas production from mashed and powdered vegetable residues by an enriched microflora in dark fermentation

This study conducted the utilization of vegetable residues by an enriched microflora inoculum to produce biohydrogen via anaerobic batch reactor. Dark fermentation processes were carried out with 3 kinds of vegetable residue substrates including broccoli (Brassica oleracea var. italica.), onion (Alium cepa Linn.), and sweet potato (Ipomoea batatas (L.) Lam). Vegetable wastes were pretreated into 2 forms, i.e. mashed and powdered vegetable, prior to the fermentation. The substrate used for the biohydrogen production were vegetable residues and inoculum at the vegetable residues/inoculum ratio of 1:1 (based on TS). The digestion processes were performed under 120 rpm speed of shaking bottle in the incubator with control temperature of 35^?C. In this work, the maximum hydrogen production was achieved by anaerobic digestion at mashed onion with bioreactor inoculum that produced total hydrogen of 424.1 mL H₂ with hydrogen yield and hydrogen concentration of 151.67 mL H₂/g VS_added and 43.54%, respectively. In addition, the hydrogen production continues took only 7 days for the vegetables blended with the bioreactor inoculum. Finally, it was found that the high potential of degradation of vegetable wastes an enriched microflora in dark fermentation also showed alternative solution to eliminate agricultural wastes to produce green energy.     

Modelling spatio-temporal movement of tourists using finite Markov chains

This paper presents a novel method for modelling the spatio-temporal movements of tourists at the macro-level using Markov chains methodology. Markov chains are used extensively in modelling random phenomena which results in a sequence of events linked together under the assumption of first-order dependence. In this paper, we utilise Markov chains to analyse the outcome and trend of events associated with spatio-temporal movement patterns. A case study was conducted on Phillip Island, which is situated in the state of Victoria, Australia, to test whether a stationary discrete absorbing Markov chain could be effectively used to model the spatio-temporal movements of tourists. The results obtained showed that this methodology can indeed be effectively used to provide information on tourist movement patterns. One significant outcome of this research is that it will assist park managers in developing better packages for tourists, and will also assist in tracking tourists’ movements using simulation based on the model used.     

Optimal software release policy with a learning factor for imperfect debugging

In this paper, we discuss a software reliability growth model with a learning factor for imperfect debugging based on a non-homogeneous Poisson process (NHPP). Parameters used in the model are estimated. An optimal release policy is obtained for a software system based on the total mean profit and reliability criteria. A random software life-cycle is also incorporated in the discussion. Numerical results are presented in the final section.