Data from software repositories are a very useful asset to building dierent kinds of models and recommender systems aimed to support software developers. Specically, the identication of likely defect-prone les (i.e., classes in Object-Oriented systems) helps in prioritizing, testing, and analysis activities. This work focuses on automated methods for labeling a class in a version as defective or not. The most used methods for automated class labeling belong to the SZZ family and fail in various circum- stances. Thus, recent studies suggest the use of aect version (AV) as provided by developers and available in the issue tracker such as JIRA. However, in many cir- cumstances, the AV might not be used because it is unavailable or inconsistent. The aim of this study is twofold: 1) to measure the AV availability and consistency in open-source projects, 2) to propose, evaluate, and compare to SZZ, a new method for labeling defective classes which is based on the idea that defects have a stable life-cycle in terms of proportion of versions needed to discover the defect and to x the defect. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, show that the AV cannot be used in the majority (51%) of defects. Therefore, it is important to investigate automated meth- ods for labeling defective classes. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes that are are aected by 60,000 defects and spread over 4,000 versions and 760,000 commits, show that the proposed method for labeling defective classes is, in average among projects and de- fects, more accurate, in terms of Precision, Kappa, F1 and MCC than all previously proposed SZZ methods. Moreover, the improvement in accuracy from combining SZZ with defects life-cycle information is statistically signicant but practically irrelevant ( overall and in average, more accurate via defects' life-cycle than any SZZ method.

Defect prediction aims at identifying software artifacts that are likely to exhibit a defect. The main purpose of defect prediction is to reduce the cost of testing and code review, by letting developers focus on specific artifacts. Several researchers have worked on improving the accuracy of defect estimation models using techniques such as tuning, re-balancing, or feature selection. Ultimately, the reliability of a prediction model depends on the quality of the dataset. Therefore effort has been spent in identifying sources of noise in the datasets, and how to deal with them, including defect misclassification and defect origin. A key component of defect prediction approaches is the attribution of a defect to a projects release. Although developers might be able to attribute a defect to a specific release, in most cases a defect is attributed to the release after which the defect has been discovered. However, in many circumstances, it can happen that a defect is only discovered several releases after its introduction. This might introduce a bias in the dataset, i.e., treating the intermediate releases as defect-free and the latter as defect-prone. We call this phenomenon a “sleeping defect”. We call “snoring” the phenomenon in which classes are affected by sleeping defects only, that would be treated as defect-free until the defect is discovered. In this work, we analyze, on data from more than 4,000 bugs and 600 releases of 20 open source projects from the Apache ecosystem for investigating: 1)the magnitude of the sleeping defects, 2) the magnitude of the snoring classes, 3)if snoring impacts the evaluation of classifiers, 4)if snoring impacts classifier accuracy, and 5)if removing the last releases of data is beneficial in reducing the negative impact of the snoring noise on classifiers accuracy. Our results show that, on average across projects: 1)most of the defects in a project slept for more than 19% of the existing releases, 2)the missing rate is more than 50% unless we remove more than 20% of the releases, 3) the relative error in measuring the classifier accuracy achieved by using a dataset with snoring is about 100% in all accuracy metrics other than AUC, 4) the presence of snoring decreases the accuracy in each of the 15 classifiers, in each of the 6 accuracy metrics. For instance, Recall, F1, Kappa and Matthews decreases by about 80%, and 5) removing one release of data is better than removing no data in all accuracy metrics. For instance, Recall, F1, Kappa and Matthews increase by about 30%.

<p> A thermodynamic investigation of interactions between solute atoms and defects (other solute atoms, electrons, phonons, and grain boundaries) has been conducted by solubility measurements of copper and gold in silicon and germanium. The major objective of the investigation was to gain a further understanding of the physical state of solute atoms and their interactions with defects in homopolar crystals. An attempt was also made to extend the theory and experimental results of equilibrium studies to kinetic phenomena associated with device manufacture. An experimental study of the kinetics of solute precipitation at dislocations was also carried out by electrical conductivity measurements. </p> <p> Original contributions which have been obtained from the results of this program are listed below. </p> <p> (1) The relative partial molar enthalpies and entropies of solution for the various systems are </p> <p> ∆Ħ_Cu (in Si)=37.3±0.5 Kcal./mole, ∆S⁻ᵉˣ_Cu(in Si)=7.1±0.4 cal./mole-ºK, </p> </p> ∆Ħ_Cu (in Ge)=41.3±0.7 Kcal./mole, ∆S⁻ᵉˣ_Cu(in Ge)=10.3±0.6 cal./mole-ºK, </p> <p> ∆Ħ_Au (in Si)=43.8±1.4 Kcal./mole, ∆S⁻ᵉˣ_Au(in Si)=6.8±1.0 cal./mole-ºK, </p> <p> ∆Ħ_Au (in Ge) ≳ 45 Kcal./mole, and ∆S⁻_Au(in Ge) ≳ 15 cal./mole-ºK. </p> <p> The partial molar enthalpy and entropy of copper in silicon with respect to Cu₃Si are 40.2±0.5 Kcal./mole and 9.7±0.5 cal./mole-°K, respectively. </p> <p> (2) Solubility measurements, metallography, and X-ray studies yielded evidence for delayed nucleation of intermediate compounds in copper-silicon diffusion couples. </p> <p> (3) The solubility of copper in vapor grown polycrystalline silicon is much greater than that in single crystal material below 800°C. The ratio of the grain boundary solubility to the single crystal solubility was estimated to be of the order of 5 x 10⁵. The high interaction energy between copper and grain boundaries in silicon (approximately 1.5 eV) was ascribed to chemical bonding. </p> <p> (4) Arsenic doping of germanium (such that the semiconductor remains intrinsic) enhances the solubility of copper in this material. This effect was quantitatively described by a theory of complex formation. </p> <p> (5) A study of the solubility of copper in p-type silicon indicated that copper is incompletely ionized in intrinsic silicon at elevated temperatures (≃1000°C). </p> <p> (6) The solubility of gold in silicon is decreased by boron doping, and this was explained on the basis of a low (less than unity) donor/acceptor ratio of substitutional gold. </p> <p> (7) The rate equation describing the precipitation of copper in silicon has a time exponent of 0.687±0.043. </p> <p> (8) Generalized phenomenological equations for ternary diffusion in covalent semiconductors were developed and it was demonstrated that information about diffusion phenomena may often be obtained from equilibrium measurements. </p> <p> (9) A quasi-steady state experiment was designed whereby copper segregated to regions of high boron concentration (in silicon) during a heat treatment operation, in qualitative agreement with theory. </p> / Thesis / Doctor of Philosophy (PhD)

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Commercial creamers that did and did not demonstrate feathering defect showed differences in feathering test, phospholipid proportion, and protein patterns of surface material of homogenized fat globules. Creams that did not feather were stable pH at in acetate buffer as low as 5.00; creams that did exhibit feathering defect were stable in acetate buffer at pH of 5.45 and higher. The proportion of phosphatidylethanoamine in feathering cream was higher than in stable cream while the proportions of phosphatidylinositol and phosphatidylserine in feathering cream were slightly less than in stable cream. Feathering cream had more caseins associated with milk fat globule than stable cream and more protein bands were observed in feathering cream than in stable cream. Feathering test with a series of acetate buffer solutions of different pHs revealed that papain treatment of cream (0.075 EU/ml) induced feathering defect near pH of 5.60 while phospholipase C treatment of cream (0.75 EU/ml) did not cause coagulation of protein and fat globules at pH 5.09. Papain-treated cream flocculated in coffee with pH 4.56 at 85°C but phospholipase C-treated cream did not. Small activity (0.075 EU/ml) of sulfhydryl protease (papain) in cream degraded most casein associated with newly formed membrane into small peptides when incubated at 4° and 21°C for 1 day. One of milk fat globule membrane proteins (butyrophilin) was hydrolyzed by papain while β-lactoglobulin was not degraded in papain-treated cream About 65-70% of phospholipid in the membrane material was degraded when 0.75 EU of phospholipase C was inoculated to 11% homogenized UHT processed cream and incubated at 4 and 21°C for 14 days. Major phospholipids (phosphatidylcholine, phosphatidylserine, phosphatidylinositol, and phosphatidylethanolamine) were hydrolyzed by phospholipase C while sphingomyelin remained intact. Heat treatment of whey protein at 80°C for 7 minutes to cover newly formed fat globule did not prevent the feathering problem in papain-treated cream. / Master of Science

Each year, 7.9 million babies are born with birth defects. Seventy percent of those could be prevented, ameliorated, or repaired; yet 3.2 million children still die by the age of three (March of Dimes Global Report 2006). We have found that non-specific maternal immune stimulation with the cytokine interferon-gamma (IFN-gamma) can successfully ameliorate some of these defects in the C57BL/6N mouse model. We have observed a reduction in the distal limb malformations syndactyly, polydactyly, and webbing by 47%, 100%, and 63% respectively when IFN-gamma is given 2 days prior to MNU administration. We have also observed that IFN-gamma works at the placental level to protect against MNU-induced damage. Trophoblast loss and associated cytokine alterations occur in gestation day (GD) 14 placenta following GD9 MNU exposure, showing that fetal-maternal communication can be hindered due to MNU. In the labyrinthine layer of the placenta, we observed multifocal fibrinous necrosis of endothelial cells due to MNU, however IFN-gamma almost completely protected the trophoblast and endothelial cells when given to the dam as an immune stimulant. To determine the genes participating in these processes, gene microarray studies were conducted. Hepatocyte growth factor (HGF), interleukin 1 beta (IL1&#914;), and insulin-like growth factor 2 (IGF2) were elucidated as genes that were significantly expressed in GD12 placenta. These genes are similar in that they are all connected to the Jak-Stat signaling pathway. These findings provide a possible mechanism for birth defect reduction by maternal immune stimulation with IFN-gamma in MNU-challenged mice. / Master of Science

BACKGROUND: Clinicians rely on radiographs and clinical exam to assess infrabony defects. However, two-dimensional radiographs have many limitations. Three-dimensional imaging has shown promise and has provided more precise measurements of defects created in skulls. The aim of this study is to compare the diagnostic efficacy of cone beam computed tomography to clinical measurements in patients presenting with infrabony defects. METHODS: The study population included 20 patients with 25 infrabony defects. Clinical measurements of pocket depth (PD), gingival margin (GM), bone sounding (BS) were obtained and PD and BS were compared to CBCT measurements. RESULTS: The average difference between the means of measurements obtained by BS and by CBCT was 1.08mm with BS always being the greater value. BS measurement was statistically significantly different with p<0.05 from CAL and CBCT values. CONCLUSION: The CBCT provided measurements that are on average 1.08mm smaller than bone sounding measurements.

The emerging 2D materials are potential solutions to the scaling of electronic devices to smaller sizes with lower energy cost and faster computing speed. Unlike traditional semiconductors e.g. Si, Ge, 2D materials do not have surface dangling bonds and the short-channel effect. A wide variety of band structure is available for different functions. The aim of the thesis is to calculate the electronic structures of several important 2D materials and study their application in particular devices, using density functional theory (DFT) which provides robust results. The Schottky barrier height (SBH) is calculated for hexagonal nitrides. The SBH has a linear relationship with metal work function but the slope does not always equal because Fermi level pinning (FLP) arises. The chemical trend of FLP is investigated. Then we show that the pinning factor of Si can be tuned by inserting an oxide interlayer, which is important in the application to dopant-free Si solar cells. Apart from contact resistance, we want to improve the conductivity of the electrode. This can be done by using a physisorbed contact layer like FeCl3, AuCl3, and SbF5 etc. to dope the graphene without making the graphene pucker so these dopants do not degrade the graphene's carrier mobility. Then we consider the defect formation of 2D HfS2 and SnS2 which are candidates in the n-type part of a tunnel FET. We found that these two materials have high mobility but there are also intrinsic defects including the S vacancy, S interstitial, and Hf/Sn interstitial. Finally, we study how to make defect states chemically inactive, namely passivation. The S vacancy is the most important defect in mechanically exfoliated 2D MoS2. We found that in the most successful superacid bis(trifluoromethane) sulfonamide (TFSI) treatment, H is the passivation agent. A symmetric adsorption geometry of 3H in the -1 charge state can remove all gap states and return the Fermi level to the midgap.

Pair programming is a programming technique in which two programmers sit literally side by side working on the same task at the same computer. One member of a pair called “driver” is in charge of writing the code. The other member plays a role of “navigator”, working on the more strategic tasks, such as looking for tactical error, thinking about overall structure, and finding better alternatives. Pair programming is claimed to improve product quality, reduce defects, and shorten time to market. On the other hand, it has been criticized on cost efficiency. To increase a body of evidence regarding the real benefits of pair programming, this thesis investigates its effect on software defects and efficiency of defect correction. The analysis bases on 14-month data of project artifacts and developers&apos; activities collected from a large Italian manufacturing company. The team of 16 developers adopts a customized version of extreme programming and practices pair programming on a daily basis. We investigate sources of defects and defect correction activities of approximately 8% of defects discovered during that time, and enhancement activities of approximately 9% of new requirements. Then we analyze whether there exists an effect of pair programming on defect rate, duration and effort of defect correction, and precision of localizing defects. The result shows that pair programming reduces the introduction of new defects when the code needs to be modified for defect corrections and enhancements.

<p dir="ltr">The study builds and evaluates three advanced neural network models—ResNet-34, EfficientNet B0, and SqueezeNet—for defect detection and classification of silicon wafer map images. The study evaluates the neural network model in two cases, binary and multi-class classifications. The binary classification, which is crucial for promptly determining whether a wafer map is defective, EfficientNet-B0 led with the highest test accuracy of 94.62% and an average accuracy of 93.2%. Similarly, in multi-class classification, necessary for pinpointing specific defect causes early in the manufacturing process, EfficientNet-B0 achieved the top test accuracy of 84.22% with an average accuracy of 84.07%. Further enhancements in the study resulted from strategic pruning of EfficientNet-B0, specifically the removal of Residual Block 2 after convolutional layer visualization revealed minimal impact on accuracy, with a reduction of just 1.33%. These modifications not only refined the learning process but also reduced the model size by 33%, thereby increasing computational efficiency. The integration of Grad-CAM++ visualizations ensured the model focused on pertinent features, thus boosting the transparency and reliability of the defect detection process. The results underscore the potential of advanced neural networks to significantly enhance the accuracy and efficiency of semiconductor manufacturing.</p>