Unicode - What you need to know

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Keheliya Gallaba

Technology Art & Photos

Unicode
What You Need To Know

Keheliya Gallaba

Terms to know

Character Sets

Arabic

Sinhala

Tamil

Chinese

Encoding

Font Faces

What does Sinhala Non-Unicode
fonts do?

What does Unicode fonts do?

Implications

Implications

Vs

Thank You!

twitter.com/keheliya
keheliya@gmail.com
April 5, 2012

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Several visualization tools have been proposed to highlight interesting software evolution phenomena. These tools help practitioners to navigate large and complex software systems, and also support researchers in studying software evolution. However, little work has explored the use of sound in the context of software evolution. In this paper, we propose the use of musical interpretation to support exploration of software evolution data. In order to generate music inspired by software evolution, we use parameter-based sonification, i.e., a mapping of dataset characteristics to sound. Our approach yields musical scores that can be played synthetically or by a symphony orchestra. In designing our approach, we address three challenges: (1) the generated music must be aesthetically pleasing, (2) the generated music must accurately reflect the changes that have occurred, and (3) a small group of musicians must be able to impersonate a large development team. We assess the feasibility of our approach using historical data from Eclipse, which yields promising results.

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Build systems orchestrate how human-readable source code is translated into executable programs. In a software project, source code changes can induce changes in the build system (aka. build co-changes). It is difficult for developers to identify when build co-changes are necessary due to the complexity of build systems. Prediction of build co-changes works well if there is a sufficient amount of training data to build a model. However, in practice, for new projects, there exists a limited number of changes. Using training data from other projects to predict the build co-changes in a new project can help improve the performance of the build co-change prediction. We refer to this problem as cross-project build co-change prediction. In this paper, we propose CroBuild, a novel cross-project build co-change prediction approach that iteratively learns new classifiers. CroBuild constructs an ensemble of classifiers by iteratively building classifiers and assigning them weights according to its prediction error rate. Given that only a small proportion of code changes are build co-changing, we also propose an imbalance-aware approach that learns a threshold boundary between those code changes that are build co-changing and those that are not in order to construct classifiers in each iteration. To examine the benefits of CroBuild, we perform experiments on 4 large datasets including Mozilla, Eclipse-core, Lucene, and Jazz, comprising a total of 50,884 changes. On average, across the 4 datasets, CroBuild achieves a F1-score of up to 0.408. We also compare CroBuild with other approaches such as a basic model, AdaBoost proposed by Freund et al., and TrAdaBoost proposed by Dai et al.. On average, across the 4 datasets, the CroBuild approach yields an improvement in F1-scores of 41.54%, 36.63%, and 36.97% over the basic model, AdaBoost, and TrAdaBoost, respectively.

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Open Source Software (OSS) components form the basis for many software systems. While the use of OSS components accelerates development, client systems must comply with the license terms of the OSS components that they use. Failure to do so exposes client system distributors to possible litigation from copyright holders. Yet despite the importance of license compliance, tool support for license compliance assessment is lacking. In this paper, we propose an approach to extract and analyze the Concrete Build Dependency Graph (CBDG) of a software system by tracing system calls that occur at build-time. Through a case study of seven open source systems, we show that the extracted CBDGs: (1) accurately classify sources as included in or excluded from deliverables with 88%-100% precision and 98%-100% recall, and (2) can uncover license compliance inconsistencies in real software systems - two of which prompted code fixes in the CUPS and FFmpeg systems.

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As a software project ages, its source code is modified to add new features, restructure existing ones, and fix defects. These source code changes often induce changes in the build system, i.e., the system that specifies how source code is translated into deliverables. However, since developers are often not familiar with the complex and occasionally archaic technologies used to specify build systems, they may not be able to identify when their source code changes require accompanying build system changes. This can cause build breakages that slow development progress and impact other developers, testers, or even users. In this paper, we mine the source and test code changes that required accompanying build changes in order to better understand this co-change relationship. We build random forest classifiers using language-agnostic and language-specific code change characteristics to explain when code-accompanying build changes are necessary based on historical trends. Case studies of the Mozilla C++ system, the Lucene and Eclipse open source Java systems, and the IBM Jazz proprietary Java system indicate that our classifiers can accurately explain when build co-changes are necessary with an AUC of 0.60-0.88. Unsurprisingly, our highly accurate C++ classifiers (AUC of 0.88) derive much of their explanatory power from indicators of structural change (e.g., was a new source file added?). On the other hand, our Java classifiers are less accurate (AUC of 0.60-0.78) because roughly 75% of Java build co-changes do not coincide with changes to the structure of a system, but rather are instigated by concerns related to release engineering, quality assurance, and general build maintenance.

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Software code review, i.e., the practice of having third-party team members critique changes to a software system, is a well-established best practice in both open source and proprietary software domains. Prior work has shown that the formal code inspections of the past tend to improve the quality of software delivered by students and small teams. However, the formal code inspection process mandates strict review criteria (e.g., in-person meetings and reviewer checklists) to ensure a base level of review quality, while the modern, lightweight code reviewing process does not. Although recent work explores the modern code review process qualitatively, little research quantitatively explores the relationship between properties of the modern code review process and software quality. Hence, in this paper, we study the relationship between software quality and: (1) code review coverage, i.e., the proportion of changes that have been code reviewed, and (2) code review participation, i.e., the degree of reviewer involvement in the code review process. Through a case study of the Qt, VTK, and ITK projects, we find that both code review coverage and participation share a significant link with software quality. Low code review coverage and participation are estimated to produce components with up to two and five additional post-release defects respectively. Our results empirically confirm the intuition that poorly reviewed code has a negative impact on software quality in large systems using modern reviewing tools.

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Unicode - What you need to know

1. Unicode What You Need To Know Keheliya Gallaba

2. Terms to know

3. Character Sets Arabic Sinhala Tamil Chinese

6. What does Sinhala Non-Unicode fonts do?

7. What does Unicode fonts do?

8. Implications

9. Implications Vs

10. Thank You! twitter.com/keheliya keheliya@gmail.com April 5, 2012