Towards Developing and Analysing Metric-Based Software Defect Severity Prediction Model

In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software. To reduce the time and effort of a tester, many machine learning models have been proposed in the literature, which use the documented defect reports to automatically predict the severity of the defective software modules. In contrast to the traditional approaches, in this work we propose a metric-based software defect severity prediction (SDSP) model that uses a self-training semi-supervised learning approach to classify the severity of the defective software modules. The approach is constructed on a mixture of unlabelled and labelled defect severity data. The self-training works on the basis of a decision tree classifier to assign the pseudo-class labels to the unlabelled instances. The predictions are promising since the self-training successfully assigns the suitable class labels to the unlabelled instances. On the other hand, numerous research studies have covered proposing prediction approaches as well as the methodological aspects of defect severity prediction models, the gap in estimating project attributes from the prediction model remains unresolved. To bridge the gap, we propose five project specific measures such as the Risk-Factor (RF), the Percent of Saved Budget (PSB), the Loss in the Saved Budget (LSB), the Remaining Service Time (RST) and Gratuitous Service Time (GST) to capture project outcomes from the predictions. Similar to the traditional measures, these measures are also calculated from the observed confusion matrix. These measures are used to analyse the impact that the prediction model has on the software project

How Far Does the Predictive Decision Impact the Software Project? The Cost, Service Time, and Failure Analysis from a Cross-Project Defect Prediction Model

Context: Cross-project defect prediction (CPDP) models are being developed to optimize the testing resources. Objectives: Proposing an ensemble classification framework for CPDP as many existing models are lacking with better performances and analysing the main objectives of CPDP from the outcomes of the proposed classification framework. Method: For the classification task, we propose a bootstrap aggregation based hybrid-inducer ensemble learning (HIEL) technique that uses probabilistic weighted majority voting (PWMV) strategy. To know the impact of HIEL on the software project, we propose three project-specific performance measures such as percent of perfect cleans (PPC), percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the predictions to calculate the amount of saved cost, remaining service time, and percent of the failures in the target project. Results: On many target projects from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure. In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the HIEL model. Conclusion: For better predictions, we recommend ensemble learning approaches for the CPDP models. And, to estimate the benefits from the CPDP models, we recommend the above project-specific performance measures

EVALUATION OF CARDIO PROTECTIVE ACTIVITY OF GALANGIN AGAINST DOXORUBICIN INDUCED CARDIOMYOPATHY

Objective: The present study was designed to investigate the cardioprotective potential of Galangin on Doxorubicin induced cardiotoxicity in rats. Methods: Albino rats used in this experiment were pretreated with vehicle, Galangin (100 & 200µg/kg) and Vit-C (20 mg/kg) for 28 days. On 25th day, a single dose of Doxorubicin (10 mg/kg, i. p) was administered to groups. After 72 h of Doxorubicin administration, ECG, serum and tissues biomarkers were evaluated. Histopathological examination of the heart was performed. Results: Doxorubicin treated rats exhibited abnormal ECG pattern followed by significant increase in CK-MB, LDH, SGOT, SGPT and LPO level and decrease in GSH, CAT, TT when compared to control rats. Pretreatment with different doses of Galangin and Vit-C significantly reduced the serum biomarkers and increased the tissue antioxidant level when compared to Doxorubicin alone treated groups. Moreover, pretreatment also improved Doxorubicin induced changes in ECG pattern and histopathology of heart. Conclusion: We conclude that the present study provides experimental evidence that Galangin has strong antioxidant activity, and it can maintain cell membrane integrity, and ameliorate oxidative stress induced by high-dose of Doxorubicin administration. These findings might be helpful to understand the beneficial effects of Galangin against myocardial injury although further study is needed to confirm its mechanism

Anti pathogenic studies of new mixed ligand metal chelates

189-196Drug discovery aimed at the methodical extermination of life-threatening bacterial infection, especially considering the emergence of multi-drug resistance of pathogenic bacteria has remained a challenge for medicinal inorganic chemistry. In this article, the mixed ligand complexes of Cu (II), Co (II), and Ni (II) containing heterocyclic ligands were synthesized and characterized by IR, LC-MS, UV, and TG-DTA. Complexes are screened for Anti-microbial activity against human pathogenic bacteria

Xylanase Production by Isolated Fungal Strain, Aspergillus fumigatus RSP-8 (MTCC 12039): Impact of Agro-industrial Material as Substrate

In the present investigation, the imperative role of agro-industrial biomass for improved xylanase production was evaluated using isolated fungal strain. This isolate was identified as Aspergillus fumigatus RSP-8 (MTCC 12039) based on morphological and 18S rRNA ribotyping and the organism was deposited in MTCC, IMTECH Chandigarh with accession number 12039. The isolated fungal strain is mesophilic in nature and produced maximum xylanase at 30 °C, at pH 7 and agitation speed of 150 rpm. Xylanase complex production titers differed with the nature and complexity of carbon source and other physiological growth parameters including aeration, growth temperature, physiological medium pH, initial inoculum levels, etc. Highest xylanase titers (73 U/mL) noticed with hemicellulose isolated from sorghum straw and least with ground nut cake as carbon source among tested agro materials such as rice bran, green gram husk, sorghum straw, groundnut cake and wheat bran. A variation of three fold enzyme titers was observed with different tested carbon sources. Supplementation of glucose as carbon source did not produce any xylanase with this fungal strain revealing the xylanase in this isolate is induced by the carbon source. Variation of hemicellulose concentration as carbon source during the fermentation altered the production xylanase titers. The study suggested that, in xylanase production by A. fumigatus RSP-8, one of the major limiting factors is substrate chemical complexity

A novel single pot reaction for substitution and cross-coupling of vinylacetate to trans-stilbenes by interlamellar montmorillonite palladium catalyst

Synthesis of trans-stilbenes on substitution and cross-coupling of vinylacetate by lnterlamellar mont morillonite ethyl silyldiphenylphosphinepalladium(II)chloride catalyst is described

Qualitative analysis of biodiesel produced by alkali catalyzed transesterification of waste cooking oil using different alcohols

330-336The present study evaluates the nature of fatty acid methyl esters (FAMEs) formed through alkali-catalyzed transesterification of waste cooking oil (WCO) using methanol, ethanol as well as in combination, where the sequential addition of ethanol followed by methanol is done keeping the molar ratio of alcohol to oil constant (5:1), with sodium hydroxide as catalyst. A substantial reduction in reaction time from 8 h to 20 min is seen in the latter case. Further, the gas chromatography/mass spectrometry (GC-MS) analysis of the transesterified oil show a significant presence of FAMEs. Transesterified oil obtained from a combination of both the solvents show substantial quantities of unsaturated FAMEs [linoleic acids (41.89%), palmitelaidic acid (7.97%)], saturated FAMEs [stearic acids (4.62%), arachidic acids (2.54%)]and minor fraction of other acids. Hence, the utilization of WCO with the use of combined solvent system for transesterification, appear to have a great potential for replacing the conventional substrates that are being used for biodiesel production without much compromising on engine modifications

Fe<SUP>3+</SUP> - Montmorillonite catalyst for selective nitration of chlorobenzene

High para-selectivity (upto 92%) and isolated yield (90%) are achieved in the nitration of chlorobenzene catalysed by Fe<SUP>3+</SUP>-exchanged K10-montmorillonite in the presence of a mixture of fuming nitric acid-acetic anhydride as nitrating agent