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documents/academic/phd_defense/approval_of_candidacy.md

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+---
+type: form
+category: academic
+person: Yanxin Lu
+date: 2017
+source: approval_of_candidacy.pdf
+---
+
+# Approval of Candidacy for the Doctoral Degree
+
+**Date:** July 10, 2017
+
+**Approved:** [Signature]
+
+The candidacy of **Yanxin Lu (ID # S01179519)**, Department of **Computer Science**, for the **Doctor of Philosophy** degree, is hereby approved. A timeline for defense of theses is located in the General Announcements.
+
+The Thesis Committee hereby appointed is:
+
+- a) Swarat Chaudhuri (Chair)
+- b) Chris Jermaine
+- c) Ankit Patel
+
+---
+
+## The student's responsibilities relating to the oral defense are to:
+
+(1) Schedule the examination. You must be registered the semester you defend.
+
+(2) See General Announcements for requirements concerning the announcement of date, time, and place of the oral examination.
+
+(3) Submit to the online thesis submission site the signed original of this form within one week after the oral examination. Students who pass the oral examination defense of thesis on or before Friday of the first week of classes of any semester do not have to register for that or any subsequent semester even though minor revisions to the final copy may be continuing. Exemption from registration is dependent upon receipt of this properly signed form in the office of Graduate Studies.
+
+(4) You have six (6) months after the successful completion of your defense to submit your thesis documents online and to the office of Graduate Studies.
+
+*Additional responsibilities of the candidate are outlined in the thesis instructions and in the Rice General Announcements.*
+
+---
+
+**Will you continue to register the semester after your defense?** No (checked)
+
+**I certify that all of the information listed on this form is correct.**
+[Student signature]
+
+---
+
+**Thesis Title (PLEASE HAND-PRINT LEGIBLY):** Corpus-Driven Systems for Program Synthesis and Refactoring
+
+## Report of the Thesis Committee:
+
+**The date of the oral defense of thesis was:** 11/19/18
+
+The committee hereby certifies that the candidate passed. Signatures MUST be **ORIGINAL**.
+
+- (a) [Signature - Swarat Chaudhuri]
+- (b) [Signature - Christopher Jermaine]
+- (c) [Signature - Ankit Patel]
+- (d) ___
+- (e) ___
+- (f) ___
+
+**Remarks:** ___
+
+---
+
+## Graduate Office Record
+
+**Thesis Accepted:** ___ (Dean of Graduate and Postdoctoral Studies)
+
+**Date:** ___

documents/academic/phd_defense/certificate_of_completion.md

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+---
+type: certificate
+category: academic
+person: Yanxin Lu
+date: 2019
+source: certificate_of_completion.pdf
+---
+
+# Certificate of Completion
+
+## of the
+
+## Survey of Earned Doctorates
+
+### Questionnaire
+
+This certifies that Yanxin Lu,
+a student at Rice University - Graduate School,
+has completed the Survey of Earned Doctorates
+on 04/18/2019.
+
+**Confirmation Code:** 227757UTRH
+
+---
+
+*Sponsored by: NSF, NIH, U.S. Department of Education, National Endowment for the Humanities*

documents/academic/phd_defense/defense_evaluation_form.md

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+---
+type: form
+category: academic
+person: Yanxin Lu
+date: 2018
+source: defense_evaluation_form.pdf
+---
+
+# Department of Computer Science
+# Rice University
+## Evaluation of Ph.D. Thesis and Defense
+
+**Student name:** Yanxin Lu
+
+**Semester entered program:** Fall 2012
+
+**Date of examination:** Mon., 11/19/18
+
+Please review the guidelines for evaluation on the reverse side of this form, and choose exactly one of the rankings for each criterion.
+
+| Criterion | Excellent | Satisfactory | Unsatisfactory |
+|---|---|---|---|
+| Problem statement | | X | |
+| Impact of work | X | | |
+| Technique | | X | |
+| Content of results | | X | |
+| Thesis text | | X | |
+| Oral Presentation | | X | |
+| Replies to questions | | X | |
+
+## Summary Evaluation
+
+I have read this thesis / proposal and recommend: **X award Ph.D.**
+
+- [ ] deny Ph.D.
+
+**Comments:**
+1. Modify thesis in consultation with Prof. Chaudhuri
+2. Expand & submit work on API refactoring
+
+**Signatures:** [Three signatures]

documents/academic/phd_defense/defense_evaluation_guidelines.md

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+---
+type: form
+category: academic
+person: Yanxin Lu
+date: 2018
+source: defense_evaluation_guidelines.pdf
+---
+
+# Department of Computer Science
+# Rice University
+## Guidelines for Evaluating Ph.D. Thesis and Defense
+
+### Problem statement
+
+- **Excellent:** Compelling problem statement that demonstrates the challenge and utility of the work, as well as theoretical or practical applications
+- **Satisfactory:** Problem is clearly stated; a case is made for utility, application
+- **Unsatisfactory:** Problem is not clearly stated; little context or justification
+
+### Impact of work
+
+- **Excellent:** Groundbreaking work or a novel problem; a thesis that will change the literature
+- **Satisfactory:** Solves an important or novel problem; quality of work merits publication in important venues
+- **Unsatisfactory:** Obvious extension to the existing literature
+
+### Technique
+
+- **Excellent:** Develops new approach to solution or applies techniques that are novel to the area
+- **Satisfactory:** Uses established techniques to solve novel problems
+- **Unsatisfactory:** Uses techniques incorrectly or inappropriately
+
+### Content of results
+
+- **Excellent:** Computer Science content is substantial and correct
+- **Satisfactory:** Content has acceptable depth and breadth & requires only minor corrections
+- **Unsatisfactory:** Content is shallow and/or contains significant errors
+
+### Thesis text
+
+- **Excellent:** Well organized text, fluent prose, and few grammatical errors
+- **Satisfactory:** Acceptable organization & text, limited grammatical errors
+- **Unsatisfactory:** Poor organization, difficult prose, or numerous grammatical errors
+
+### Oral Presentation
+
+- **Excellent:** Engaging, polished presentation with well crafted visual aides that illustrate key results; includes a substantial conclusion
+- **Satisfactory:** Professional presentation on a par with a solid conference talk; includes a coherent project narrative and conclusion
+- **Unsatisfactory:** Too much or too little detail; unclear about project goals and direction; incoherent slides; candidate reads from slides
+
+### Replies to questions
+
+- **Excellent:** Complete answers that demonstrate a deep understanding of the discipline that extends beyond the thesis
+- **Satisfactory:** Competent answers that illustrate a facility with the issues and techniques immediately relevant to the thesis
+- **Unsatisfactory:** Answers reveal a limited comprehension of the work and its context

documents/academic/phd_defense/signed_title_page.md

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+---
+type: thesis
+category: academic
+person: Yanxin Lu
+date: 2019
+source: signed_title_page.pdf
+---
+
+# RICE UNIVERSITY
+
+## Corpus-Driven Systems for Program Synthesis and Refactoring
+
+by
+
+### Yanxin Lu
+
+A Thesis Submitted
+in Partial Fulfillment of the
+Requirements for the Degree
+
+### Doctor of Philosophy
+
+---
+
+Approved, Thesis Committee:
+
+**Swarat Chaudhuri, Chair**
+Associate Professor of Computer Science
+[Signature]
+
+**Christopher Jermaine**
+Professor of Computer Science
+[Signature]
+
+**Ankit B. Patel**
+Assistant Professor of Electrical and Computer Engineering
+[Signature]
+
+---
+
+Houston, Texas
+
+April, 2019

documents/academic/phd_defense/thesis_final.md

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+---
+type: thesis
+category: academic
+person: Yanxin Lu
+date: 2019
+source: thesis_final.pdf
+---
+
+# RICE UNIVERSITY
+
+## Corpus-Driven Systems for Program Synthesis and Refactoring
+
+by
+
+### Yanxin Lu
+
+A Thesis Submitted
+in Partial Fulfillment of the
+Requirements for the Degree
+
+### Doctor of Philosophy
+
+---
+
+Approved, Thesis Committee:
+
+**Swarat Chaudhuri, Chair**
+Associate Professor of Computer Science
+[Signature]
+
+**Christopher Jermaine**
+Professor of Computer Science
+[Signature]
+
+**Ankit B. Patel**
+Assistant Professor of Electrical and Computer Engineering
+[Signature]
+
+Houston, Texas
+
+April, 2019
+
+---
+
+## Abstract
+
+### Corpus-Driven Systems for Program Synthesis and Refactoring
+
+by
+
+Yanxin Lu
+
+Software development is a difficult task. Programmers need to work with many small components in large software projects which typically contain more than thousands of lines of code. To make software development manageable, developers and researchers have deployed various programming systems and tools. These include the ones that can facilitate refactoring existing source code and even generate programs automatically. One problem with traditional program synthesis tools is that they cannot generate practical results when given large specifications due to its high complexity of the underlying problem. Furthermore, existing refactoring systems can only refactor individual components separately and fail to instantiate complete programs. To overcome these problems, we can learn useful patterns and idioms from large code corpora using machine learning techniques. Researchers have used "big code" and developed novel and practical programming tools such as Bayou [1] and JSNice [2]. In this thesis, we present two data-driven programming systems for software reuse and refactoring.
+
+We first introduce *program splicing*, a programming methodology that aims to automate the workflow of copying, pasting, and modifying code available online. Here, the programmer starts by writing a "draft" that mixes unfinished code, natural language comments, and correctness requirements. A program synthesizer that interacts with a large, searchable database of program snippets is used to automatically complete the draft into a program that meets the requirements. Our evaluation uses the system in a suite of everyday programming tasks and includes a comparison with a state-of-the-art competing approach as well as a user study. The results point to the broad scope and scalability of program splicing and indicate that the approach can significantly boost programmer productivity.
+
+Next, we propose an algorithm that automates the process of *API refactoring*, where the goal is to rewrite an API call sequence into another sequence that only uses the API calls defined in the target library without modifying the functionality. We solve the problem of API refactoring by combining the techniques of API translation and API sequence synthesis. Specifically, we first translate original API calls into a set of new API calls defined in the target library. Then we use an API synthesizer to generate a complete program that uses the translated API calls. We evaluated our algorithm on a diverse set of benchmark problems, and our algorithm can refactor API sequences with high accuracy.
+
+Although the evaluations of the techniques presented in this thesis are quite optimistic, we believe that there is room for improvement by using more sophisticated language model and advanced search algorithm for program splicing. To improve our API refactoring method, one can train statistical models by using existing API call sequence pairs. Besides these potential improvements, many problems related to "big code" still remain, and the potential of using a data-driven method to help programming is enormous.
+
+---
+
+## Contents
+
+- Abstract ... ii
+- List of Illustrations ... vi
+- List of Tables ... viii
+
+### 1 Introduction ... 1
+- 1.1 Program reuse via splicing ... 6
+- 1.2 API refactoring using natural language and API synthesizer ... 8
+- 1.3 Summary ... 11
+
+### 2 Program Splicing ... 12
+- 2.1 Introduction ... 12
+- 2.2 Motivating Examples ... 14
+  - 2.2.1 Reading a Matrix from a CSV File ... 14
+  - 2.2.2 Face Detection using OpenCV ... 19
+- 2.3 Problem formulation ... 21
+- 2.4 Method ... 24
+  - 2.4.1 Searching for programs ... 24
+  - 2.4.2 Program completion ... 26
+- 2.5 Evaluation ... 31
+  - 2.5.1 Benchmarks ... 32
+  - 2.5.2 Experiments ... 35
+- 2.6 Summary ... 45
+
+### 3 API Refactoring ... 46
+- 3.1 Introduction ... 46
+- 3.2 Motivating Examples ... 49
+- 3.3 Problem Definition ... 54
+- 3.4 Method ... 55
+  - 3.4.1 API Translation ... 56
+  - 3.4.2 API Call Sequence Synthesis ... 58
+- 3.5 Evaluation ... 62
+  - 3.5.1 Benchmarks ... 62
+  - 3.5.2 Experiments ... 63
+  - 3.5.3 Limitations ... 69
+- 3.6 Summary ... 69
+
+### 4 Related Work ... 71
+- 4.1 Program Synthesis and Reuse ... 71
+- 4.2 Data-driven Program Synthesis ... 74
+- 4.3 Code Search ... 77
+- 4.4 API Refactoring and Translation ... 81
+
+### 5 Conclusion and Future Work ... 85
+
+### Bibliography ... 89
+
+---
+
+*Note: This is the final signed version of the thesis (1.6MB). The full thesis contains 95+ pages of technical content including figures, tables, algorithms, code examples, experimental results, and bibliography. The complete content is preserved in the PDF.*

documents/academic/phd_defense/thesis_main.md

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+---
+type: thesis
+category: academic
+person: Yanxin Lu
+date: 2019
+source: thesis_main.pdf
+---
+
+# RICE UNIVERSITY
+
+## Corpus-Driven Systems for Program Synthesis and Refactoring
+
+by
+
+### Yanxin Lu
+
+A Thesis Submitted
+in Partial Fulfillment of the
+Requirements for the Degree
+
+### Doctor of Philosophy
+
+---
+
+Approved, Thesis Committee:
+
+**Swarat Chaudhuri, Chair**
+Associate Professor of Computer Science
+
+**Christopher Jermaine**
+Professor of Computer Science
+
+**Ankit B. Patel**
+Assistant Professor of Electrical and Computer Engineering
+
+Houston, Texas
+
+April, 2019
+
+---
+
+## Abstract
+
+### Corpus-Driven Systems for Program Synthesis and Refactoring
+
+by
+
+Yanxin Lu
+
+Software development is a difficult task. Programmers need to work with many small components in large software projects which typically contain more than thousands of lines of code. To make software development manageable, developers and researchers have deployed various programming systems and tools. These include the ones that can facilitate refactoring existing source code and even generate programs automatically. One problem with traditional program synthesis tools is that they cannot generate practical results when given large specifications due to its high complexity of the underlying problem. Furthermore, existing refactoring systems can only refactor individual components separately and fail to instantiate complete programs. To overcome these problems, we can learn useful patterns and idioms from large code corpora using machine learning techniques. Researchers have used "big code" and developed novel and practical programming tools such as Bayou [1] and JSNice [2]. In this thesis, we present two data-driven programming systems for software reuse and refactoring.
+
+We first introduce *program splicing*, a programming methodology that aims to automate the workflow of copying, pasting, and modifying code available online. Here, the programmer starts by writing a "draft" that mixes unfinished code, natural language comments, and correctness requirements. A program synthesizer that interacts with a large, searchable database of program snippets is used to automatically complete the draft into a program that meets the requirements. Our evaluation uses the system in a suite of everyday programming tasks and includes a comparison with a state-of-the-art competing approach as well as a user study. The results point to the broad scope and scalability of program splicing and indicate that the approach can significantly boost programmer productivity.
+
+Next, we propose an algorithm that automates the process of *API refactoring*, where the goal is to rewrite an API call sequence into another sequence that only uses the API calls defined in the target library without modifying the functionality. We solve the problem of API refactoring by combining the techniques of API translation and API sequence synthesis. Specifically, we first translate original API calls into a set of new API calls defined in the target library. Then we use an API synthesizer to generate a complete program that uses the translated API calls. We evaluated our algorithm on a diverse set of benchmark problems, and our algorithm can refactor API sequences with high accuracy.
+
+Although the evaluations of the techniques presented in this thesis are quite optimistic, we believe that there is room for improvement by using more sophisticated language model and advanced search algorithm for program splicing. To improve our API refactoring method, one can train statistical models by using existing API call sequence pairs. Besides these potential improvements, many problems related to "big code" still remain, and the potential of using a data-driven method to help programming is enormous.
+
+---
+
+## Contents
+
+- Abstract ... ii
+- List of Illustrations ... vi
+- List of Tables ... viii
+
+### 1 Introduction ... 1
+- 1.1 Program reuse via splicing ... 6
+- 1.2 API refactoring using natural language and API synthesizer ... 8
+- 1.3 Summary ... 11
+
+### 2 Program Splicing ... 12
+- 2.1 Introduction ... 12
+- 2.2 Motivating Examples ... 14
+  - 2.2.1 Reading a Matrix from a CSV File ... 14
+  - 2.2.2 Face Detection using OpenCV ... 19
+- 2.3 Problem formulation ... 21
+- 2.4 Method ... 24
+  - 2.4.1 Searching for programs ... 24
+  - 2.4.2 Program completion ... 26
+- 2.5 Evaluation ... 31
+  - 2.5.1 Benchmarks ... 32
+  - 2.5.2 Experiments ... 35
+- 2.6 Summary ... 45
+
+### 3 API Refactoring ... 46
+- 3.1 Introduction ... 46
+- 3.2 Motivating Examples ... 49
+- 3.3 Problem Definition ... 54
+- 3.4 Method ... 55
+  - 3.4.1 API Translation ... 56
+  - 3.4.2 API Call Sequence Synthesis ... 58
+- 3.5 Evaluation ... 62
+  - 3.5.1 Benchmarks ... 62
+  - 3.5.2 Experiments ... 63
+  - 3.5.3 Limitations ... 69
+- 3.6 Summary ... 69
+
+### 4 Related Work ... 71
+- 4.1 Program Synthesis and Reuse ... 71
+- 4.2 Data-driven Program Synthesis ... 74
+- 4.3 Code Search ... 77
+- 4.4 API Refactoring and Translation ... 81
+
+### 5 Conclusion and Future Work ... 85
+
+### Bibliography ... 89
+
+---
+
+*Note: This is the unsigned/pre-defense version of the thesis (963KB). The full thesis contains 95+ pages of technical content including figures, tables, algorithms, code examples, experimental results, and bibliography. The complete content is preserved in the PDF.*

documents/academic/phd_defense/thesis_submittal_confirmation.md

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+---
+type: form
+category: academic
+person: Yanxin Lu
+date: 2018
+source: thesis_submittal_confirmation.pdf
+---
+
+# Thesis and Dissertation Submission
+
+**URL:** https://thesis.rice.edu/submit/2787/complete
+
+**Date:** 11/19/18, 15:04
+
+**User:** Yanxin Lu
+
+---
+
+Home / Submissions / Complete
+
+# Submittal Complete
+
+Thank you for uploading your thesis via Rice University's Thesis and Dissertation Management System.
+
+If you have not already done so, you may log back into this site at any time to submit your final, corrected, signed thesis as well as your administrative documents:
+
+- Scan of your signed Approval of Candidacy form
+- Survey of Earned Doctorates Completion Certificate (doctoral degrees only)
+
+After you have done so, please bring the following documents in hard copy to Allen Center 323 between 1:30 - 3:30 Monday-Friday:
+
+- Original Approval of Candidacy form
+- Two signed, original copies of your thesis' title page
+
+You are welcome to ask someone to deliver these documents on your behalf. If you are unable to submit your forms during these hours, please email graduate@rice.edu to request an appointment. Hours will extend during the two weeks prior to each semester's deadline for thesis submission.
+
+Please be advised that your file is not complete and cannot be reviewed until we have your uploaded thesis, your administrative files, and the signed original documents.
+
+After reading the instructions on our website, http://graduate.rice.edu/submitthesis, please contact your department coordinator or graduate@rice.edu if you have any questions.
+
+If you need a document confirming your submission for employment purposes, please email graduate@rice.edu with your request.
+
+Best,
+
+The Office of Graduate and Postdoctoral Studies and the
+Center for Digital Scholarship
+Rice University
+
+View submission status
+
+---
+
+*Instructions | FAQ | Managed by Rice GPS and Fondren Library | Contact Us*