Model-based tests of truisms

Raffo, David; Setamanit, Siri-on; Hu, Ying; Tootoonian, S.

doi:10.1109/ase.2002.1115012

Cited by 25 publications

(19 citation statements)

References 38 publications

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“…Textile dyeing industries in Malaysia was important and was ranked as the first in the colouration of fiber [4]. Wastewater from the textile industries was declared as one of the major sources of wastewater pollutions in ASEAN countries, after considering both volumes discharge and effluent composition [5].…”

Section: Introductionmentioning

confidence: 99%

Preparation of activated carbon from tamarind seeds and Methylene blue (MB) removal

Jamion¹,

Hashim²

2018

J. Fundam and Appl Sci.

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In this study, thedye treatment which is methylene blue ascertained with the activated carbon that prepared from the tamarind seed (Tamarindusindica). The conditions used to prepare activated carbon, using phosphoric acid as an activating agent at temperature 500 conditions may be attributed to enhance the surface area and pores development of TSC.Single point BET surface area (S and FESEM analysis showed that the pores development and formation were mostly in circle and oval pattern. The best conditions for TSC sample to adsorb MB effectively were at 50 mL of MB with concentration 120 ppm at temperature, 323 K by u maximum adsorption capacity for MB dye solution was 102.77 mg g In this study, thedye treatment which is methylene blue (MB) as water pollutants the activated carbon that prepared from the tamarind seed ). The conditions used to prepare activated carbon, (TSC) were activat using phosphoric acid as an activating agent at temperature 500 o C for four hours. These conditions may be attributed to enhance the surface area and pores development of TSC.Single point BET surface area (S BET ) analysis gave the surface area of TSC was and FESEM analysis showed that the pores development and formation were mostly in circle and oval pattern. The best conditions for TSC sample to adsorb MB effectively were at 50 mL of MB with concentration 120 ppm at temperature, 323 K by using 0.04 g of TSC. The maximum adsorption capacity for MB dye solution was 102.77 mg g -1 .; methylene blue; adsorption; phosphoric acid.

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Section: Introductionmentioning

confidence: 99%

Preparation of activated carbon from tamarind seeds and Methylene blue (MB) removal

Jamion¹,

Hashim²

2018

J. Fundam and Appl Sci.

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“…Code reviews, are labor-intensive and one member can inspect only 8 to 20 LOC/minute. Same review process will be repeated for all members of the review team, which can be as large as four or six [7]. One effective approach for early identification of faulty modules is software fault prediction which investigates and learns the characteristics of individual code and design segments to predict the fault-prone modules [4].…”

Section: Related Workmentioning

confidence: 99%

Software Fault Prediction at Design Phase

Singh¹,

Verma²,

Vyas³

2014

Journal of Electrical Engineering and Technology

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-Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naïve Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

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“…By contrast, other methods such as manual code reviews are laborintensive. Depending on the review methods 8 to 20 LOC/minute can be inspected and this effort repeats for all members of the review team, which can be as large as four or six [22].…”

Section: But Does It Work?mentioning

confidence: 99%

On the value of learning from defect dense components for software defect prediction

Zhang

Nelson

2010

Proceedings of the 6th International Conference on Predictive Models in Software Engineering

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BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusing on the defect-rich portions of the training sets. METHOD: Defect data CM1, KC1, MC1, PC1, PC3 was separated into components. A subset of the projects (selected at random) were set aside for testing. Training sets were generated for a NaiveBayes classifier in two ways. In sample the dense treatment, the components with higher than the median number of defective modules were used for training. In the standard treatment, modules from any component were used for training. Both samples were run against the test set and evaluated using recall, probability of false alarm, and precision. In addition, under sampling and over sampling was performed on the defect data. Each method was repeated in a 10-by-10 cross-validation experiment. RESULTS: Prediction models learned from defect dense components out-performed standard method, under sampling, as well as over sampling. In statistical rankings based on recall, probability of false alarm, and precision, models learned from dense components won 4-5 times more often than any other method, and also lost the least amount of times. CONCLUSIONS: Given training data where most of the defects exist in small numbers of components, better defect predictors can be trained from the defect dense components.

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Model-based tests of truisms

Cited by 25 publications

References 38 publications

Preparation of activated carbon from tamarind seeds and Methylene blue (MB) removal

Preparation of activated carbon from tamarind seeds and Methylene blue (MB) removal

Software Fault Prediction at Design Phase

On the value of learning from defect dense components for software defect prediction

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