A Systematic Literature Review and Meta-analysis on Cross Project Defect Prediction

Background: Cross project defect prediction (CPDP) recently gained considerable attention, yet there are no systematic efforts to analyse existing empirical evidence. Objective: To synthesise literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances. Further, we aim to assess the performance of CPDP vs. within project DP models. Method: We conducted a systematic literature review. Results from primary studies are synthesised (thematic, meta-analysis) to answer research questions. Results: We identified 30 primary studies passing quality assessment. Performance measures, except precision, vary with the choice of metrics. Recall, precision, f-measure, and AUC are the most common measures. Models based on Nearest-Neighbour and Decision Tree tend to perform well in CPDP, whereas the popular na¨ıve Bayes yield average performance. Performance of ensembles varies greatly across f-measure and AUC. Data approaches address CPDP challenges using row/column processing, which improve CPDP in terms of recall at the cost of precision. This is observed in multiple occasions including the meta-analysis of CPDP vs. WPDP. NASA and Jureczko datasets seem to favour CPDP over WPDP more frequently. Conclusion: CPDP is still a challenge and requires more research before trustworthy applications can take place. We provide guidelines for further research

A preliminary study on Naegleria species in water bodies of Kurunegala district, Sri Lanka

Introduction and Objective: Species belonging to the genus Naegleria are free-living ubiquitous protozoa. They have been isolated from most regions of the world. N. fowleri causes an acute, fulminant and rapidly fatal infection involving the central nervous system (CNS) in humans. It is known as primary amoebic meningoencephalitis (PAM). Infection is generally acquired while swimming, diving and total submersion for bathing in freshwater-lakes and ponds. Many inland fresh water bodies are present in Sri Lanka. These water bodies are frequently used by people for their daily needs. However, studies have not yet been conducted to determine the prevalence of Naegleria species occurring in local water bodies. The present study was therefore, carried out to isolate Naegleria species from selected water bodies located in four Divisional Secretariat (DS) divisions in the Kurunegala district, Sri Lanka.Methods: Two different sites (clear and turbid water) of each tank were selected for sampling. Two water samples (surface water and deep water) were collected from each site (4 samples from one tank). Altogether, eighty water samples were collected from 20 tanks. Culture, enflagellation test and staining were done to detect Naegleria species. ArcGIS 10.3 and MINITAB (14) software were used for the data analysis.Results: Flagella transformation was observed in 19 (47.5%) surface water samples and 11 (27.5%) deep water samples. Of 20 tanks, 10 were positive for Naegleria species.Conclusions: Findings of the present study suggest that more specific genotyping studies are needed to confirm the presence of pathogenic N. fowleri in the study area.</p

AGRO CLIMATIC RISK ASSESSMENT IN HAMBANTOTA REGION

In order to eliminate the risk on crop production in Hambantota region, spatial andtemporal variation of rainfall were analyzed based on rainfall magnitude, duration, riskand onset. The rainfall data over 42 years (1960-2002) in six rain gauge stations inHarnbantota region was assessed10 mm weekly rainfall at 75% probability level method was used to find the wet weeks ineach station throughout the year. 10 mm weekly rainfall at 50% probability level wasused for rainfall onset identification. Farmer survey was conducted to find the existingcropping calendar. The amount of rainfall, which accumulated on the date of cropcommencement, was identified using Forward accumulation methodThe results revealed that mean annual rainfall is decreasing in Harnbantota region. Allstations were recorded less than 20% wet weeks. It reveals that the high risk involveswith rain fed crop cultivation in Harnbantota region. Based on the 10 mm weekly rainfallat 50% probability level, rainfall onset for yolo and moho seasons varies from 11th to 16thweek and from 37th to 42nd week respectively.The farmers could be able to minimize theirrigation need using these rainfall onset weeks as their crop commencement weeks.Based on the farmer survey, crop commencement week in moho season varies between39th and 41"t week and farmers rarely cultivate during yolo season. According to theforward accumulation method at 75% probability level the amount of water accumulatedat crop commencement time was 75mm. The results indicate that the cropcommencement week based on farmer survey coincided with calculated rainfall onsetduring moho season.

A systematic literature review on cross-project defect prediction

Background: Cross-project defect prediction, which provides feasibility to build defect prediction models in the case of lack of local data repositories, have been gaining attention within research community recently. Many studies have pursued improving predictive performance of cross-project defect prediction models by mitigating challenges related to cross-project defect prediction. However there has been no attempt to analyse the empirical evidence on cross-project defect prediction models in a systematic way. Objective: The objective of this study is to summarise and synthesise the existing cross-project defect prediction studies in order to identify what kind of independent variables, modelling techniques, performance evaluation criteria and different approaches are used in building cross-project defect prediction models. Further, this study aims to explore the predictive performance of cross-project defect prediction models compared to within-project defect prediction models. Method: A systematic literature review was conducted to identify 30 relevant primary studies. Then qualitative and quantitative results of those studies were synthesized to answer defined research questions. Results: The majority of the Cross Project Defect Prediction (CPDP) models have been constructed using combinations of different types of independent variables. The models that perform well tend to be using combinations of different types of independent variables. Models based on Nearest Neighbour (NN) and Decision Tree (DTree) appear to perform well in CPDP context. Most commonly used Naive Bayes (NB) seemed to having average performance among other modelling techniques. Recall, precision, F-measure, probability of false alarm (pf) and Area Under Curve (AUC) are the commonly used performance metrics in cross-project context. Filtering and data transformation are also frequently used approaches in the cross-project context. The majority of the CPDP approaches address one or more data related issues using various row and column processing methods. Models appear to be performing well when filtering approach is used and model is built based on NB. Further, models perform well with data transformation approach is used and model is built based on Support Vector Machine (SVM). There is no significant difference in performance of CPDP models compared with Within Project Defect Prediction (WPDP) model performance. CPDP model perform well in majority cases in terms of recall. Conclusion: There are various types of independent variables, modelling techniques, performance evaluation criteria that have been used in cross-project defect prediction context. Cross-project defect prediction model performance is influenced by the way it is being built. Cross-project defect prediction still remains as a challenge, but they can achieve comparative predictive performance as within-project defect prediction models when the factors influencing the performance are optimized

Skills Developments of Labourers to Achieve the Successful Project Delivery in the Sri Lankan Construction Industry

Construction industry can be identified as a labour intensive industry which carries heavy reliance upon the skills of labourers. Skilful labour force is one of the vital elements for the continuity and successful implementation of construction projects. It has been identified that the performance of labourers neither been measured quantitatively nor qualitatively in the Sri Lankan construction industry. Hence, there is no standard to recruit labour with a perfect understanding of their level of performance and consequently, the industry will face many difficulties in identifying the right crew to complete the project as per the required standard within specified time and money. Hence, this study attempts to address the problems associated with skills of labourers in building projects. Accordingly, the aim of this research is to develop a framework to enhance the skills of labourers to enable the successful project delivery in the Sri Lankan construction industry.The survey approach was selected as the most suitable research approach due to the quantitative nature of the study. Thirty questionnaires and ten semi-structured interviews were conducted by random selection of project managers, quantity surveyors, site engineers for the interviews and labourers for questionnaire survey. Content analysis was used to analyse data collected from interviews and the data collected from questionnaires were analysed using statistical methods such as binomial test and Relative Importance Index (RII). Based on the analysis, the conclusions were drawn and recommendations were put forward.The findings of the study revealed that less guidance, less motivation on labourers and poor examination of their skills are the major hindrances for the skills development. Migration, technology innovation and poor image on employment condition were identified as the root causes of the skilled labour shortage. Moreover, the study revealed that unskilled labourers are working as skilled labourers due to the existing shortage of skilled labourers and it will negatively affect the quality and standard of the outcomes, cause high material wastage and spend long time for project completion. By considering the aforementioned facts, the research ultimately introduced a framework to develop skills of labourers to achieve the successful project delivery in Sri Lankan construction industry

Detection of red lesions in retinal images using image processing and machine learning techniques

Diabetic Retinopathy (DR) is a diabetes complication that causes damage to the blood vessels of the light sensitive tissue at the back of the eye. All the people who are suffering from diabetes have a high risk of subjecting to DR which may lead to total blindness. Red lesions, cotton-wool spots and exudates are symptoms of non proliferative diabetic retinopathy which is the early stage of diabetic retinopathy. When the disease develops to proliferative diabetic retinopathy fluid leaking from retinal capillaries and the formation of new vessels on the surface of the retina happens. At this stage there is a very low possibility of preventing total blindness. Therefore, early detection of DR is important to prevent vision loss. So, if there is an easy way of detecting early signs of DR accurately that will be beneficial. Red lesion detection is more important for the early identification of DR. In this paper, we are proposing a method for the automated detection of red lesions in retinal images using image processing techniques and machine learning. The developed algorithm has sensitivity and specificity of 92.05% and 88.68% respectively

Investigation on self-discharge mechanism of neutral aqueous electrolyte based electric double layer supercapacitor

Supercapacitors are capable of holding electrical charges that are much larger than conventional dielectric capacitors. A supercapacitor was constructed using active carbon electrodes, deposited on metal current collectors and they were separated by a non-conducting membranes soaked in an electrolyte. An electrical double layer is developed at the interface between active carbon and the electrolyte. The electrical properties of constructed capacitors were investigated to identify the drawbacks of these systems. Self-discharge has been an inescapable issue of electrical double layer type super capacitors and it reduces cell voltage of the capacitor. The data collected from fabricated supercapacitor was used to model the self-discharge behaviour with the aim of understanding the discharge mechanism of these capacitors. It was found that in addition to ohmic leakage mechanism, there is another dominant mechanism

A systematic literature review and meta-analysis on cross project defect prediction

Abstract Background: Cross project defect prediction (CPDP) recently gained considerable attention, yet there are no systematic efforts to analyse existing empirical evidence. Objective: To synthesise literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances. Further, we aim to assess the performance of CPDP versus within project DP models. Method: We conducted a systematic literature review. Results from primary studies are synthesised (thematic, meta-analysis) to answer research questions. Results: We identified 30 primary studies passing quality assessment. Performance measures, except precision, vary with the choice of metrics. Recall, precision, f-measure, and AUC are the most common measures. Models based on Nearest-Neighbour and Decision Tree tend to perform well in CPDP, whereas the popular naïve Bayes yields average performance. Performance of ensembles varies greatly across f-measure and AUC. Data approaches address CPDP challenges using row/column processing, which improve CPDP in terms of recall at the cost of precision. This is observed in multiple occasions including the meta-analysis of CPDP versus WPDP. NASA and Jureczko datasets seem to favour CPDP over WPDP more frequently. Conclusion: CPDP is still a challenge and requires more research before trustworthy applications can take place. We provide guidelines for further research