An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models

The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed with respect to defect updated date (updated date DS). These defects datasets are modelled by eight SRGMs; Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model. These models are chosen due to their widespread use in the literature. The SRGMs are fitted to both types of defects datasets of each project and the their fitting and prediction capabilities are analysed in order to study the OSS reliability growth with respect to defects creation and defects updating time because defect analysis can be used as a constructive reliability predictor. Results show that SRGMs fitting capabilities and prediction qualities directly increase when defects creation date is used for developing OSS defect datasets to characterize the reliability growth of OSS. Hence OSS reliability growth can be characterized with SRGM in a better way if the defect creation date is taken instead of defects updating (fixing) date while developing OSS defects datasets in their reliability modellin

Physiological Approaches To An Improved Understanding Of Waterlogging Tolerance In Cotton

Climatic variability, typified by erratic heavy-rainfall events, causes waterlogging in intensively irrigated crops and is exacerbated when crops are grown under warm temperature regimes on soils with poor internal drainage. Irrigated cotton is often grown in precisely these conditions around the world, exposing it to waterlogging-induced yield losses after substantial summer rainfall. This requires a deeper understanding of the basis of waterlogging tolerance and its relevance to cotton. The yield penalty depends on soil type, phenological stage and the cumulative period of root exposure to air-filled porosities below 10%. Events in the soil include O2 deficiency in the root zone, which changes the redox state of nutrients, making them unavailable (e.g. nitrogen) or potentially toxic (e.g. manganese) for plants. Furthermore, root-derived hormones that are transported through the xylem have long been associated with oxygen deficits. These belowground effects (impaired root growth, nutrient uptake and transport, hormonal signalling) have impacts in the shoots, interfering with canopy development, photosynthesis (Pn) and radiation use efficiency. Compared with the more waterlogging tolerant cereals, cotton does not have identified adaptations to waterlogging in the root zone, forming no conspicuous root aerenchyma and having low fermentative activity. These factors contribute substantially to the sensitivity of cotton to sustained periods of waterlogging. Despite significant advances in cotton production systems, limited efforts have been made to improve cotton performance in waterlogged soils. Management practices such as soil aeration, scheduling irrigation and fertiliser application are practiced to reduce waterlogging damage. However, little information is available on physiological responses of cotton to waterlogging. Cotton plants respond to a variety of stresses through a complex signalling network of hormones. Understanding the biosynthesis and regulation of these hormones (e.g. ethylene) in cotton tissue offers an opportunity to modulate cotton performance under stressful environments. The central research question was: can waterlogging-induced yield losses in cotton be minimised by modulating key physiological processes? This thesis aims to investigate the physiological mechanisms of waterlogging damage to cotton and devise targets for increased waterlogging tolerance. Since heavy rainfall events are often associated with cloudy conditions, restricting light availability to waterlogged (WL) cotton, it was hypothesised that shade would amplify yield losses in WL cotton. The initial field studies investigated how conditions of low incident light (i.e. shade) can modify the growth and yield of cotton crops experiencing waterlogging. The objective of these experiments was to study physiological mechanisms of waterlogging- and shade-induced damage to cotton. Either early or late in the reproductive phase, the crop was waterlogged (96 h and 120 h, in 2012-13 and 2013-14 cotton seasons, respectively) and/or shaded (6 d or 9 d in 2012-13 and 2013-14, respectively). Waterlogging at the early reproductive phase significantly reduced lint yield (17% averaged across both seasons) of cotton, although shade-induced yield losses (18%) were only significant in 2013-14. Shade significantly exacerbated yield losses under moderate waterlogging only, when the impact of waterlogging damage was modest (2013-14). More intense waterlogging impaired yield independently of light levels. Yield reductions in stressed cotton were mainly the consequence of accelerated fruit abscission and fewer fruiting nodes produced. Plants had lower leaf nitrogen levels and photosynthetic rates after waterlogging and/or shade treatments and produced fewer fruiting nodes, while stress-induced ethylene most likely acted by stimulating fruit abscission. Although, long-term shade increased specific leaf area (30%), leaf N (20%) and stomatal conductance (5%) immediately following 5 d of WL, it did not restore shoot growth, node formation or lint yield because of suppressed photosynthetic performance and carbohydrate supply. Thus, it can be concluded that interaction between waterlogging and shade depends on the intensity of individual stress. After observing limited effects of shade to a severely WL cotton crops, further studies were focused on exploring the physiological mechanisms of waterlogging damage alone in more detail. Due to indeterminate growth of cotton, it was hypothesised that different canopy layers would respond variably to soil waterlogging. Field experiments were conducted with the objective of understanding how waterlogging influences growth and yield of cotton across canopy layers. The crop was waterlogged at early (WLearly, 77 d after planting [DAP]) and late reproductive phases (WLlate, 101 DAP) for 120 h. Plants were tagged, and data from different canopy layers (bottom eight (MSN1-8), middle five (MSN9-13), and upper main stem nodes (MSN14+) were collected one day (post-WL) and 7 d after termination of waterlogging (post-recovery). Both waterlogging events significantly reduced post-WL dry biomass, leaf N and fruit development on MSN1-8. In addition, WLearly significantly reduced photosynthesis and increased total soluble sugars in MSN1-8 and MSN14+ leaves, although MSN14+ leaves restored photosynthesis, N level and sugars at recovery. These results suggested that WL plants could maintain photosynthesis in the upper leaves, possibly by transporting N from the lower leaves. Seed cotton yield reduction (22%) under WLearly was mainly the result of fruit loss from the first fruiting position of the upper and lowest sympodial fruiting branches (FB1-5 and FB11+), and WL plants continued to produce additional fruits on 2nd and 3rd position located on FB1-5. Despite the recovery in growth through improved photosynthesis and leaf N concentration, there was no yield recovery on FB11+ suggesting that plants used additional assimilates for the growth of established fruits. No significant yield reduction in response to WLlate suggested that the established cotton bolls were less sensitive to abscission across all canopy layers. Variable response of different canopy layers to soil waterlogging indicated the need of studying the effect of any stress on the whole canopy rather than top Field experiments clearly demonstrated that accelerated abscission of young fruits in WL cotton is the major cause of yield reduction, and the process is potentially regulated by ethylene. Thus, it was hypothesised that waterlogging damage to cotton can be minimised by blocking ethylene production. Glasshouse and field experiments were conducted with an objective to optimise application rates (0, 50, 100 and 150 [active ingredient, ai] ha-1) and time (pre- and post-waterlogging) of an anti-ethylene agent, aminoethoxyvinylglycine (AVG) for WL cotton. The glasshouse study suggested that AVG (ReTain®, 100-150 g [ai] ha-1) applied 24 h prior to waterlogging can increase growth and fruit retention of WL and non-waterlogged (NWL) cotton. The positive effects of AVG were further validated in two years of field studies. The crop was exposed to WLearly and WLlate. The data from field experiments suggested that AVG (125 g [ai] ha-1) applied at the early reproductive phase of cotton can significantly increase cotton yield under WL (13%, averaged across two years) and NWL (9%, averaged across two years) environments. Yield increase in AVG-treated cotton was associated with increased boll numbers, boll weight and fruit retention. On the other hand, no further improvement in cotton yield under higher AVG concentration (150 g [ai] ha-1) indicated the saturation of AVG on ethylene inhibition. Thus, appropriate AVG concentration and application timing may help to overcome waterlogging-induced yield losses in cotton production systems. The role of ethylene in regulating cotton yield was further explored in glasshouse experiments conducted at Macquarie University, Australia. The objective of the first glasshouse experiment was to investigate the relationships between ethylene accumulation and waterlogging sensitivity of two cotton cultivars, Sicot 71BRF (moderately waterlogging tolerant) and LA 887 (waterlogging sensitive). It was hypothesised that elevated ethylene accumulation in cotton tissues is responsible for waterlogging damage to cotton. The plants were grown in a clay-loam soil, and exposed to waterlogging at early reproductive phase (53 DAP). One d prior to waterlogging, the shoots were sprayed with AVG (830 ppm≈ AVG 125 g [ai] ha-1). Continuous waterlogging for 2 weeks accelerated the shedding of leaves and fruits. As the duration of waterlogging increased, shoot growth rate, biomass accumulation, Pn and gs were all reduced. Growth of LA 887 was more severely impaired than Sicot 71BRF, with a decline in leaf Pn and gs after just 4 h of waterlogging. Waterlogging inhibited allocation of N to the youngest fully expanded leaves, Pn and biomass accumulation, while it accelerated ethylene production promoting leaf and fruit abscission. AVG blocked the ethylene accumulation in leaves and subsequently improved leaf growth, N acquisition and photosynthetic parameters. In addition, AVG enhanced fruit production of both cotton cultivars under WL and NWL conditions. Higher ethylene production in cotton was linked with fruit abscission, implying that AVG-induced ethylene inhibition could potentially limit yield losses in WL cotton. Since yield losses in WL cotton were strongly associated with photosynthesis inhibition and accelerated ethylene production, it was hypothesised that waterlogging damage can be mitigated by modulating ethylene and carbon metabolism in cotton. The second glasshouse experiment at the Macquarie University investigated the role of ethylene as a major yield limiting factor for WL cotton. The objective of this experiment was to investigate the response to waterlogging tolerance by manipulating carbon and ethylene metabolism. Two cotton genotypes, varying in lint production and sensitivity to ethylene, namely Empire, a fully linted cotton cultivar and 5B, a lintless mutant line (lintless), were compared in a glasshouse study. At the peak reproductive phase (66 DAP), plants were exposed to waterlogging for 9 d and allowed to recover for 7 d after termination of waterlogging. Ethylene synthesis was inhibited by spraying AVG (830 ppm) one day prior to waterlogging and carbon dioxide enrichment (eCO2) was applied at the start of reproductive growth. The effect of these treatments on fruit production and distribution was studied in both cotton genotypes. By the end of the experiment, lintless plants produced significantly more fruits compared with Empire under all treatment conditions. In addition, the growth and fruiting pattern of the two cotton genotypes varied significantly in response to waterlogging, AVG and eCO2. Waterlogging significantly increased the release of ethylene from different tissues of both cotton genotypes, although fruit production was significantly inhibited only in Empire. Consistently, AVG significantly reduced waterlogging-induced abscission of fruits, mainly in Empire, by suppressing ethylene synthesis. Elevated CO2 promoted plant growth and fruit production in both genotypes, and was more effective in lintless than in Empire plants. Limited damage to fruits in lintless, despite increased production of ethylene during waterlogging, suggested that fruit abscission was generally associated with ethylene action, and that lintless was ethylene insensitive. The lintless produced more fruits than Empire, providing additional sinks that enhanced the response to CO2 enrichment. By contrast, eCO2 induced ethylene production in reproductive organs of the ethylene-sensitive Empire plants and subsequently affected fruit abscission, counteracting the positive effects of CO2 enrichment on reproductive development. These experiments provide conclusive evidence that increased ethylene biosynthesis in cotton plants and photosynthetic inhibition are the major reasons for yield reduction in cotton exposed to WL environments. The data contribute to the understanding of mechanisms through which waterlogging induces yield losses in cotton and suggest techniques for ameliorating this damage. Future studies should focus on characterisation of ethylene-responsive genes and their regulation in cotton with a prospect of increasing stress tolerance. This thesis elucidates the physiological mechanisms underlying the responses of cotton to soil waterlogging. WL cotton plants exhibited an ability to maintain yield in the top layers of canopy by remobilising nutrients. Photosynthetic inhibition and abscission of young fruits were the major reasons of waterlogging-induced yield losses in cotton, which can be minimised by suppressing ethylene or enhancing carbon metabolism

Reliability in open source software

Open Source Software is a component or an application whose source code is freely accessible and changeable by the users, subject to constraints expressed in a number of licensing modes. It implies a global alliance for developing quality software with quick bug fixing along with quick evolution of the software features. In the recent year tendency toward adoption of OSS in industrial projects has swiftly increased. Many commercial products use OSS in various fields such as embedded systems, web management systems, and mobile software’s. In addition to these, many OSSs are modified and adopted in software products. According to Netcarf survey more than 58% web servers are using an open source web server, Apache. The swift increase in the taking on of the open source technology is due to its availability, and affordability. Recent empirical research published by Forrester highlighted that although many European software companies have a clear OSS adoption strategy; there are fears and questions about the adoption. All these fears and concerns can be traced back to the quality and reliability of OSS. Reliability is one of the more important characteristics of software quality when considered for commercial use. It is defined as the probability of failure free operation of software for a specified period of time in a specified environment (IEEE Std. 1633-2008). While open source projects routinely provide information about community activity, number of developers and the number of users or downloads, this is not enough to convey information about reliability. Software reliability growth models (SRGM) are frequently used in the literature for the characterization of reliability in industrial software. These models assume that reliability grows after a defect has been detected and fixed. SRGM is a prominent class of software reliability models (SRM). SRM is a mathematical expression that specifies the general form of the software failure process as a function of factors such as fault introduction, fault removal, and the operational environment. Due to defect identification and removal the failure rate (failures per unit of time) of a software system generally decreases over time. Software reliability modeling is done to estimate the form of the curve of the failure rate by statistically estimating the parameters associated with the selected model. The purpose of this measure is twofold: 1) to estimate the extra test time required to meet a specified reliability objective and 2) to identify the expected reliability of the software after release (IEEE Std. 1633-2008). SRGM can be applied to guide the test board in their decision of whether to stop or continue the testing. These models are grouped into concave and S-Shaped models on the basis of assumption about cumulative failure occurrence pattern. The S-Shaped models assume that the occurrence pattern of cumulative number of failures is S-Shaped: initially the testers are not familiar with the product, then they become more familiar and hence there is a slow increase in fault removing. As the testers’ skills improve the rate of uncovering defects increases quickly and then levels off as the residual errors become more difficult to remove. In the concave shaped models the increase in failure intensity reaches a peak before a decrease in failure pattern is observed. Therefore the concave models indicate that the failure intensity is expected to decrease exponentially after a peak was reached. From exhaustive study of the literature I come across three research gaps: SRGM have widely been used for reliability characterization of closed source software (CSS), but 1) there is no universally applicable model that can be applied in all cases, 2) applicability of SRGM for OSS is unclear and 3) there is no agreement on how to select the best model among several alternative models, and no specific empirical methodologies have been proposed, especially for OSS. My PhD work mainly focuses on these three research gaps. In first step, focusing on the first research gap, I analyzed comparatively eight SRGM, including Musa Okumoto, Inflection S-Shaped, Geol Okumoto, Delayed S-Shaped, Logistic, Gompertz and Generalized Geol, in term of their fitting and prediction capabilities. These models have selected due to their wide spread use and they are the most representative in their category. For this study 38 failure datasets of 38 projects have been used. Among 38 projects, 6 were OSS and 32 were CSS. In 32 CSS datasets 22 were from testing phase and remaining 10 were from operational phase (i.e. field). The outcomes show that Musa Okumoto remains the best for CSS projects while Inflection S-Shaped and Gompertz remain best for OSS projects. Apart from that we observe that concave models outperform for CSS and S-Shaped outperform for OSS projects. In the second step, focusing on the second research gap, reliability growth of OSS projects was compared with that of CSS projects. For this purpose 25 OSS and 22 CSS projects were selected with related defect data. Eight SRGM were fitted to the defect data of selected projects and the reliability growth was analyzed with respect to fitted models. I found that the entire selected models fitted to OSS projects defect data in the same manner as that of CSS projects and hence it confirms that OSS projects reliability grows similarly to that of CSS projects. However, I observed that for OSS S-Shaped models outperform and for CSS concave shaped models outperform. To overcome the third research gap I proposed a method that selects the best SRGM among several alternative models for predicting the residuals of an OSS. The method helps the practitioners in deciding whether to adopt an OSS component, or not in a project. We test the method empirically by applying it to twenty one different releases of seven OSS projects. From the validation results it is clear that the method selects the best model 17 times out of 21. In the remaining four it selects the second best model

A Comparative Analysis of Software Reliability Growth Models using defects data of Closed and Open Source Software

The purpose of this study is to compare the fitting (goodness of fit) and prediction capability of eight Software Reliability Growth Models (SRGM) using fifty different failure Data sets. These data sets contain defect data collected from system test phase, operational phase (field defects) and Open Source Software (OSS) projects. The failure data are modelled by eight SRGM (Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model). These models are chosen due to their prevalence among many software reliability models. The results can be summarized as follows -Fitting capability: Musa Okumoto fits all data sets, but all models fit all the OSS datasets -Prediction capability: Musa Okumoto, Inflection S- Shaped and Goel Okumoto are the best predictors for industrial data sets, Gompertz and Yamada are the best predictors for OSS data sets - Fitting and prediction capability: Musa Okumoto and Inflection are the best performers on industrial datasets. However this happens only on slightly more than 50% of the datasets. Gompertz and Inflection are the best performers for all OSS dataset

Microwave parameters for bitumen emulsion and its application in highway engineering

Bitumen emulsion is used as a bonding material between two layers and partially acts as a water proofing agent; it gained more importance when there were environmental concerns with cutbacks bitumen. A very few properties of bitumen emulsions are known so far from its physical and chemical prospective. However, a new method to measure its dielectric properties i.e. permittivity by applying electromagnetic waves (microwaves) with open ended coaxial probe (OCP) method was investigated in this study at different temperatures and frequency of 8 to 12 GHz. After examining the physical properties of certain bitumen emulsion a correlation was established on the basis of dielectric constant (permittivity) with the existing conventional property viscosity at temperature of 25, 40, 50 and 60°C. On the basis of these properties different characteristics of the bitumen emulsion were found and hence, a correlation was established to predict its behavior. A good correlation factor was found for the four types of samples used in the study which were 0.98 for SS-1K, 0.99 for MS-1K, 1.00 for RS-1K and 0.99 for K1-40. This study has provided an effective parameter to measure and predict the behavior of bitumen emulsion at different temperature, which can be applied in highway industry

Impact of Overall injustice on Employee Performance: Moderating Effect of Supportive Leadership Style

The purpose of this paper is to study the impact of overall injustice on the performance of employees working in the Private sectors and to investigate how supportive leadership style in supervisors can increase employee performance when they are under high stress due to injustice perceptions. Data was collected through questionnaires that were designed and distributed to the employees working in private sectors. Sample size of 250 was equally distributed in the two sectors. This measured the perceived level of injustice related stress and its possible effect on employee performance. Supportive leadership style has a significant effect on performance of employee and increases the performance but injustice may or may not affect employee performance. Injustice is sometimes not given much importance due to low magnitude or external causes of injustice, so it is not always negatively related to employee performance. The research expands our knowledge of supportive leadership and tended to focus that how supportive leadership style in supervisors can increase employee performance working in private sectors. Public sector organizations should also be studied and sample size should be increased to cover large number of organizations. By expanding the range of organizations in the study would add credibility to the findings. Supportive leadership style plays an important role in the overall performance of an employee. Organizations need to improve leadership skills in supervisors to achieve positive outcomes and increased productivity

Impact of Overall injustice on Employee Performance: Moderating Effect of Supportive Leadership Style

The purpose of this paper is to study the impact of overall injustice on the performance of employees working in the Private sectors and to investigate how supportive leadership style in supervisors can increase employee performance when they are under high stress due to injustice perceptions. Data was collected through questionnaires that were designed and distributed to the employees working in private sectors. Sample size of 250 was equally distributed in the two sectors. This measured the perceived level of injustice related stress and its possible effect on employee performance. Supportive leadership style has a significant effect on performance of employee and increases the performance but injustice may or may not affect employee performance. Injustice is sometimes not given much importance due to low magnitude or external causes of injustice, so it is not always negatively related to employee performance. The research expands our knowledge of supportive leadership and tended to focus that how supportive leadership style in supervisors can increase employee performance working in private sectors. Public sector organizations should also be studied and sample size should be increased to cover large number of organizations. By expanding the range of organizations in the study would add credibility to the findings. Supportive leadership style plays an important role in the overall performance of an employee. Organizations need to improve leadership skills in supervisors to achieve positive outcomes and increased productivity

Neuroprotection with metformin and thymoquinone against ethanol-induced apoptotic neurodegeneration in prenatal rat cortical neurons

<p>Abstract</p> <p>Background</p> <p>Exposure to ethanol during early development triggers severe neuronal death by activating multiple stress pathways and causes neurological disorders, such as fetal alcohol effects or fetal alcohol syndrome. This study investigated the effect of ethanol on intracellular events that predispose developing neurons for apoptosis via calcium-mediated signaling. Although the underlying molecular mechanisms of ethanol neurotoxicity are not completely determined, mitochondrial dysfunction, altered calcium homeostasis and apoptosis-related proteins have been implicated in ethanol neurotoxicity. The present study was designed to evaluate the neuroprotective mechanisms of metformin (Met) and thymoquinone (TQ) during ethanol toxicity in rat prenatal cortical neurons at gestational day (GD) 17.5.</p> <p>Results</p> <p>We found that Met and TQ, separately and synergistically, increased cell viability after ethanol (100 mM) exposure for 12 hours and attenuated the elevation of cytosolic free calcium [Ca²⁺]_c. Furthermore, Met and TQ maintained normal physiological mitochondrial transmembrane potential (Δψ_M), which is typically lowered by ethanol exposure. Increased cytosolic free [Ca²⁺]_cand lowered mitochondrial transmembrane potential after ethanol exposure significantly decreased the expression of a key anti-apoptotic protein (Bcl-2), increased expression of Bax, and stimulated the release of cytochrome-c from mitochondria. Met and TQ treatment inhibited the apoptotic cascade by increasing Bcl-2 expression. These compounds also repressed the activation of caspase-9 and caspase-3 and reduced the cleavage of PARP-1. Morphological conformation of cell death was assessed by TUNEL, Fluoro-Jade-B, and PI staining. These staining methods demonstrated more cell death after ethanol treatment, while Met, TQ or Met plus TQ prevented ethanol-induced apoptotic cell death.</p> <p>Conclusion</p> <p>These findings suggested that Met and TQ are strong protective agents against ethanol-induced neuronal apoptosis in primary rat cortical neurons. The collective data demonstrated that Met and TQ have the potential to ameliorate ethanol neurotoxicity and revealed a possible protective target mechanism for the damaging effects of ethanol during early brain development.</p