The question of what makes a person, or a society develop has been asked countless of times. There are many factors that can influence this. This research will seek to explore whether culture matters in relation to a person’s personal development. The present study interviewed twelve Afghan immigrants living in Sweden and investigated whether they felt like culture adjustment was necessary in order for them to develop. The study is using an abductive approach as the respondents' answers are analyzed in firsthand while the theoretical framework is used as a guidance throughout the research. Lawrence E. Harrisons theory was conducted as the theoretical framework. Harrison was a professor who believed that culture affects a person's ability to develop (Harrison, 2000, p. 2). This research contributed to the prevailing literature by analyzing this topic from a bottom-up perspective by interviewing the Afghan immigrants and presenting their perspectives. Further research regarding this topic would be appreciated as the research gap still needs research to be filled. The findings of this study suggest that the twelve Afghan immigrants do not feel like a person has to adapt to Swedish culture to develop. By rather mixing different cultures and being openminded they feel has helped them to develop. Further, Harrison is arguably too categorical, hence, it is not feasible to categorize people by culture because development is different from person to person.

Indiana University-Purdue University Indianapolis (IUPUI) / Static code analysis tools are known to flag a large number of false positives. A false positive is a warning message generated by a static code analysis tool for a location in the source code that does not have any known problems. This thesis presents our approach and results in identifying and documenting false positives generated by static code analysis tools. The goal of our study was to understand the different kinds of false positives generated so we can (1) automatically determine if a warning message from a static code analysis tool truly indicates an error, and (2) reduce the number of false positives developers must triage. We used two open-source tools and one commercial tool in our study. Our approach led to a hierarchy of 14 core false positive patterns, with some patterns appearing in multiple variations. We implemented checkers to identify the code structures of false positive patterns and to eliminate them from the output of the tools. Preliminary results showed that we were able to reduce the number of warnings by 14.0%-99.9% with a precision of 94.2%-100.0% by applying our false positive filters in different cases.

Indiana University-Purdue University Indianapolis (IUPUI) / This dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engineering metrics using a highlighted synthetic source code named the Juliet test suite. From this evaluation, we concluded that the SCA tools report plenty of false positive warnings that need a manual inspection. Then we generated a number of datasets from the source code that forced the SCA tool to generate either true positive, false positive, or false negative warnings. The datasets, then, were used to train four of ML classifiers in order to classify the collected warnings from the synthetic source code. From the experimental results of the ML classifiers, we observed that the classifier that built using the Random Forests (RF) technique outperformed the rest of the classifiers. Lastly, using this classifier and an instance-based transfer learning technique, we ranked a number of warnings that were aggregated from various open-source software projects. The experimental results show that the proposed approach to reduce the cost of the manual inspection of the false positive warnings outperformed the random ranking algorithm and was highly correlated with the ranked list that the optimal ranking algorithm generated.

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京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第18200号 / 人健博第17号 / 新制||人健||2(附属図書館) / 31058 / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 黒木 裕士, 教授 三谷 章, 教授 杉本 直三 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

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The main purpose of exception handling mechanisms is to improve software robustness by handling exceptions when they occur. However, empirical evidence indicates that improper implementation of exception handling code can be a source of faults in software systems. There is still limited empirical knowledge about the relationship between exception handling code and defects. In this dissertation, we present three case studies investigating defect densities of exception handling code. The results show that in every system under study, the defect density of exception handling code was significantly higher than the defect density of overall source code and normal code. The ability to predict the location of faults can assist in directing quality enhancement efforts to modules that are likely to have faults. This information can be used to guide test plans, narrow the test space, and improve software quality. We hypothesize that complicated exception handling structure is a predictive factor that is associated with defects. To the best of our knowledge, no study has addressed the relationship between the attributes of exception handling method call structures and defect occurrence, nor has prior work addressed fault prediction. We extract exception-based software metrics from the structural attributes of exception handling call graphs. To find out whether there are patterns of relationship between exception-based software metrics and fault-proneness, we propose a defect prediction model using exception handling call structures. We apply the J48 algorithm, which is the Java implementation of the C4.5 algorithm, to build exception defect prediction models. In two out of three systems under study, the results reveal that there are logical patterns of relationships between most class level exception metrics and fault-proneness. The accuracy of our prediction models is comparable to the results of defect prediction model studies in the literature. It was observed that our approach has somewhat worse predictive accuracy when a system has low average defects per class.

The present study focuses on the evolving microstructure of Mg alloy AZ31. The material is subjected to channel die compression at room temperature to simulate a reduction stage in the rolling process. Samples are annealed to provoke recovery, static recrystallization, and grain growth. Annealing is carried out at three temperatures for times ranging from 10s to 10,000s. The material’s response is exhibited through the use of data collection methods such as microhardness, optical microscopy, and electron backscatter diffraction (EBSD). Methodology behind experimentation and data collection techniques are documented in detail. Conclusions are made about the effects of the compression and annealing processes on the material’s microstructure. The Johnson-Mehl-Avrami-Kolmogorov (JMAK) model is introduced, and a simple recrystallization kinetics plot is attempted.

This study investigated the lateral shear resistance of multi-staple joints, static and fatigue bending moment resistances of stapled gusset-plate joints, and fatigue bending performance of full size sofa structural members in three oriented strandboard (OSB) materials. It is concluded that the lateral shear resistance capacity of multi-staple joints in OSB can be predicted with power equations including single-staple lateral resistance capacity, number of staples, and material density. The bending moment resistance of stapled gusset plate joints in OSB can be predicted by mechanical models including the lateral resistance property of multi-staple joints. The static moment capacity study of a gusset-plate joint concluded that its maximum moment resistance is 2 times of its corresponding proportional limit moment resistance. Experimental results indicated that there was no significant difference in lateral shear resistance among vertically-aligned multi-staple joints constructed of three OSB materials even though there were significant differences in lateral resistance among single-staple joints in these three materials. However, there were significant differences in lateral resistances among horizontally-aligned multi-staple joints in three OSB materials. Fatigue test results concluded that the ratio of static to passed fatigue moment capacity of gusset-plate joints was 2.6. The proposed equations reasonably estimated fatigue life of less brittle OSB materials subjected to cyclic stepped and constant fatigue loads but underestimated the brittle OSB material. The ratios of material modulus of rupture to passed fatigue stress were 2.17 and 2.25 for cyclic stepped and constant cyclic tests, respectively.