2022年度 シラバス学部

ソフトウェア・エンジニアリング関連科目

2022/06/27  現在

科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3年
単位数
/Credits
3.0
責任者
/Coordinator
イエン ニール ユーウェン
担当教員名
/Instructor
イエン ニール ユーウェン, 山田 竜平
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/31
授業の概要
/Course outline
Nowadays, the Web is not only the source of information for the end users. Companies migrate more of their business activities to Web based systems. We are facing increasing demands for professionals who can design large Web systems. Web engineering is a relatively new term in computer science. It can be defined as a discipline of systematic development of Web applications.
授業の目的と到達目標
/Objectives and attainment
goals
The aim of this course is to study current concepts and methods for Web application engineering.
授業スケジュール
/Class schedule
Lecture 1:
Introduction

Lecture 2-3:
A Web engineering process

Lecture 4:
Communication

Lecture 5-6:
Web App Architectures

Lecture 7:
Introduction to WordPress

Lecture 8-9:
Creating A Website with WordPress

Lecture 10:
Responsive Web Design

Lecture 11-12:
Universal Design for Web

Lecture 13:
About the Future

Lecture 14:
Discussions
教科書
/Textbook(s)
No designated textbook is required but students are encouraged to follow the content of

The Modern Web: Multi-Device Web Development with HTML5, CSS3, and JavaScript by Peter Gasston, No Starch Press, 2013.
Dart 1 for Everyone Fast, Flexible, Structured Code for the Modern Web by Chris Strom, The Pragmatic Programmers, LLC., 2014.
Anatomy of a web application using node.js, ExpressJS, MongoDB & Backbone.js by Jason Crol, 2015.
Knockout.js: Building Dynamic Client-Side Web Applications by Jamine Munro, O’Reilly Media, 2015.
成績評価の方法・基準
/Grading method/criteria
The final grade will be calculated based on the following contributions:
Exercises - 45%,
Presentations/Reports - 25%
Active Participation during lectures - 20%,
Final examination - 10%.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
3年
単位数
/Credits
3.0
責任者
/Coordinator
渡部 有隆
担当教員名
/Instructor
ヴァジェニン アレクサンダー, 渡部 有隆
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/12
授業の概要
/Course outline
This course covers many current topics of interest in software engineering. Some of the topics covered are formal methods to specify requirements of software systems, software reuse, software maintenance, software maintenance models, and evaluation of processes, products, and resources. It includes Advanced Treatment of Selected Software Engineering issues: Software Maintenance, Software Configuration Management, Software Re-engineering, Managing People, Critical Systems Development, User Interface Design and Evaluation, Emerging Technologies like Visual Programming, Security Engineering, and other advanced topics including student presentation topic as well as student engineering topic.
授業の目的と到達目標
/Objectives and attainment
goals
The objective of the course is to impart knowledge to students about methods in software development. The methods range from how to precisely specify software requirements to how to evaluate the methods and their products and required resources. This is achieved in three parts: first, lectures are given on several current topics of interest; second, students are asked to make a presentation on a topic interesting to them; and third, students are given a programming project to design and implement a system using the Object-Oriented (Java) and Visual Programming paradigm.
授業スケジュール
/Class schedule
Lecture 1: The Nature of Software Engineering
Topics to study:
- How did software engineering become a term?
- Is there a good technical solution to software development problems?
- How and why are agile methods considered more people-affirming?
- Compare software engineering with other professions.

Lecture 2: Revisioning Software
Topics to study:
- Collaborative development problems,
- Revision control,
- VCS terminology,
- Collaborative development and conflict resolution.

Lecture 3: The Human Factors in Software Engineering
Topics to study:
- Human Diversity;
- Limits to Thinking;
- Knowledge Modeling;
- Personality Types;
- Human Factors Engineering.

Lecture 4: The Managing People and Team Work
Topics to study:
- Selecting and Motivating Staff
- Ego-less Programming
- Managing Groups
- The People Capability Maturity Model

Lecture 5-6: User Interface Design and Evaluation
Topics to study:
- User Interface Design Principles
- User Interaction Styles
- Information Presentation
- GUI Features
- Message System Features
- System Documentation
- User Interface Design Process
- Interface Evaluation

Lecture 7: Visual Programming Systems
Topics to study:
- Terminology
- Classification and Theory
- A Review of Visual Programming Systems

Lecture 8: Midterm

Lecture 9: Software change: Maintenance and Architectural Evolution
Topics to study:
-Program evolution dynamics
-Software maintenance
-Architectural evolution

Lecture 10: Software re-engineering
Topics to study:
-Source code translation
-Reverse engineering
-Program structure improvement
-Program modularization
-Data re-engineering

Lecture 11: Critical systems development
Topics to study:
-Dependable processes
-Dependable programming
-Fault tolerance
-Fault tolerant architectures

Lecture 12: Software Security Engineering
Topics to study:
-Security concepts
-Security risk management
-Design for security
-System survivability

Lecture 13: Code Writing
Topics to study:
-Organization and Purposes
-Quality requirements
-Algorithmic complexity
-Methodologies
-Measuring language usage
-Debugging

Lecture 14: Student presentations
教科書
/Textbook(s)
1.Software Engineering, 5-9th editions by Ian Sommerville, publisher: Addision-Wesley
2.Human Aspects of Software Engineering by J.E. Tomayko and O. Hazzan, Charles River Media Inc., 2004
3. User Interface Design and Evaluation by D. Stone, C. Jarrett, M.Woodroffe, Sh. Mincha
4. Lecture notes distributed by the instructor will be developed from materials collected from books, journals and proceedings papers.
成績評価の方法・基準
/Grading method/criteria
Your final grade includes the following parts
1). All Lab Exercises: 50 points in total including:
* Presentation Topic: 20 points
* Engineering Topic: 30 points
     - All reports submitted: 20 points
     - The GUI Interface designed:10 points
2). Midterm Test: 25 points
3). Final Exam: 25 points
履修上の留意点
/Note for course registration
It would be good if students have knowledge about basics of Software Engineering, Programming in Java  and C.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructor Alexander Vazhenin has practical working experience. He worked for the Computer Center of Siberian Division of the Russian Academy of Sciences for 15 years where he was involved in R&D of software design and operating systems. Based on his experience, he can teach the basics of Software Engineering.

1. Course WWW-site: http://sealpv2.u-aizu.ac.jp/
2. Software Engineering: A Practitioner's Approach, 4th edition by Roger S. Pressman, publisher: McGraw-Hill
3. B.B. Agarwal, S.P. Tayal, M. Gupta, Software Engineering & Testing, Computer Science Series.

Class:Lecture,Exercises


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  前期 /First Semester
対象学年
/Course for;
4年
単位数
/Credits
3.0
責任者
/Coordinator
吉岡 廉太郎
担当教員名
/Instructor
吉岡 廉太郎, 川口 立喜
推奨トラック
/Recommended track
先修科目
/Essential courses
事前に学んでおいてほしい科目一覧(下記科目内容の一部ないし全部を既知として授業を進めます)
FU14ソフトウェア工学概論
更新日/Last updated on 2022/01/26
授業の概要
/Course outline
履修学生でチームを組み、顧客の求めるソフトウェアの開発を行うことを通してソフトウェア工学に対する理解を深める授業です。
授業では、顧客から実際に注文を受け、一つの注文を一つの学生チームが受け持ちます。通常3~10名程度のチームになります。各チームは、顧客の要望を聞き取るヒアリングから始め、開発すべきソフトウェアの要件を決定するところから始めます。その後、設計、開発、テストの各開発工程を実施し、第14回授業内で開発作業の概要および成果物の発表を行い、顧客に納品します。
各チームには産業界から派遣されるコーチが付き開発実務のアドバイスを行います。毎週教員・コーチに進捗を報告するとともに、必要に応じて顧客と打合せを持ちます。開発の各行程は成果物(ドキュメント類)で管理し、各工程を終了する度に提出していきます。顧客も参加する中間レビューと最終レビューでは、作業時間数、成果物の量、質などを定量的に分析して報告を行います。
顧客によって定められた機能と品質を満たし、顧客にとって最良のソフトウェアを提供するため、創造と努力の限りを尽くします。そのため、必要となる新たな知識・技術を身につけたり、なんども修正したりするため、授業外の作業時間は当然発生します。チームを構成する全学生の自主的で主体的な取り組みが必須です。
授業の目的と到達目標
/Objectives and attainment
goals
1. 一定の(要求された)機能と品質を満足するソフトウェアを開発するにあたっての課題に気づき、それらを解決する基本的な手法を学ぶ。
2. リソース(人、物、時間など)が限られた中でソフトウェアを完成するためにプロジェクト管理が必要であることを実感し、ソフトウェア工学で学んだ各工程(要求定義・分析、設計、開発、テスト)の手法を実践する。
3. 異分野の技術にまたがる問題の特定や正確な情報伝達など、現実世界で直面する流動的な環境への迅速な対応の必要性を体験し、それに対応するための手法を学ぶ。
履修学生は、上記1~3を通して、比較的大規模で、実用に耐えうるソフトウェアの開発の課題について理解することができる。
授業スケジュール
/Class schedule
各回の授業(3コマ)では、
1. 各チームによる進捗報告(各10分)
2. 進捗報告に対するフィードバック
3. その週の作業の進め方と留意点に関する講義
4. チーム毎に顧客との打合せ
を行います。

第1回:プロジェクト立ち上げ, 計画立案
チーム内での役割分担を決め、プロジェクト管理システムの準備、顧客からの注文依頼を聞き、要求定義以降の開発プロセスを実行するためのプロジェクト計画を立案する
第2回:現地調査
 顧客の要求を理解し、課題についての理解を深めるため、現場の調査を行う。(顧客の都合等により時期は前後する)
第3回:要件定義
 課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。
第4回:要件定義
課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。積極的にプロトタイプを作成してイメージの共有とすり合わせを行う。
第5回:要件定義
課題から要求を正確に把握し、明確な要求として整理し、システム化する範囲を顧客と合意する。
第6回:中間レビュー・デモ
顧客が要件定義の成果物を精査し、以降のフェーズに着手してよいかどうかの判断を行う。
第7回:分析
要件定義工程の成果物から、要求仕様を論理的に分析し、実現手段を検討する。
第8回:分析
 要件定義工程の成果物から、要求仕様を論理的に分析し、実現手段を検討する。
第9回:設計
システムに必要な機能やオブジェクトの構造や振る舞いを、実装を考慮して詳細化する。
第10回:設計
システムに必要な機能やオブジェクトの構造や振る舞いを、実装を考慮して詳細化する。
第11回:開発
ソフトウェアのソースコードを作成する。
第12回:開発&テスト
ソフトウェアのソースコードを作成し、テストを行う。
第13回:テスト
 ソフトウェアのテストを行う。
第14回:最終レビュー
顧客がテスト結果および、詳細設計以降の成果物の内容を精査し、受け入れが妥当か否かを判断する。

※積極的にプロトタイピングを行いながら、チームごとにスケジュール管理と作業を行うので進捗は変わってきますが、中間レビューと最終レビューの日付は変えられません。
教科書
/Textbook(s)
※必要に応じて講義資料を配布する
成績評価の方法・基準
/Grading method/criteria
期末試験は行わず、以下の項目で評価する
1. 中間レビューと最終レビューでの発表25%
2. 成果物(ドキュメントおよびソフトウェア)25%
3. 個人レポート 40%
4. 活動への参加と貢献度 10%
※各学生の貢献度は進捗報告とプロジェクト管理システムで確認する。
※評価には顧客とコーチの意見も取り入れる。
※個人レポートでは、プロジェクトでの活動・貢献内容やソフトウェア工学とプロジェクトマネジメントの知識・理解に関する質問について論じてもらいます。
履修上の留意点
/Note for course registration
• 次の授業を履修していることを前提とした授業です。
FU14ソフトウェア工学概論
IE03ソフトウェア総合演習Ⅰ
IE04ソフトウェア総合演習Ⅱ
• 3下旬~授業開始前の期間に履修希望者に対して実施する事前面談を必ず受けてください。
• プロジェクトごとに必要とされる知識・技術が異なります。
• 開発に必要な技術・知識(設計、プログラミング等)について、スキルが足りない場合には、時間外の自助努力を求めます。
• 授業およびミーティングへの参加は必須です。無断での欠席、遅刻、途中退席は、やむをえない場合を除いてマイナス評価します。
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
https://www.u-aizu.ac.jp/enpit/record/2020_software.html
https://www.u-aizu.ac.jp/enpit/record/2019_software.html
https://www.u-aizu.ac.jp/enpit/record/2018_software.html
https://www.u-aizu.ac.jp/enpit/record/2017_software.html

実務経験有り:企業でソフトウェア開発に従事している技術者が講師として毎回の授業に参加し、学生の開発活動に対するレビュー、評価、アドバイスを行います。
現役のソフトウェア技術者(30年以上の実務経験)とソフトウェア開発に従事した経験豊富な教員が共同で授業を行います。


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
3年
単位数
/Credits
3.0
責任者
/Coordinator
モズゴボイ マキシム
担当教員名
/Instructor
モズゴボイ マキシム
推奨トラック
/Recommended track
先修科目
/Essential courses
Courses preferred to be learned prior to this course (This course assumes understanding of entire or partial content of the following courses)
PL03 JAVA Programming I
更新日/Last updated on 2022/02/07
授業の概要
/Course outline
Concurrent programs take advantage of modern multicore and multiprocessor machines to implement algorithms that run concurrently (in parallel) to achieve higher performance and better user experience. Distributed computing brings this idea to the next level, dealing with the systems made up of independent computers, linked by a network.

At the present time, both concurrent and distributed systems are widespread, due to high popularity of multicore machines and computer networks. However, the design and implementation of such systems and corresponding software remains a challenging task. We have to know how to coordinate independent processes to achieve high performance and avoid common pitfalls.

The goal of this course is to introduce the basics of concurrent and distributed systems design and implementation. We will cover a number of classical and modern approaches to this problem, paying special attention to the practical aspects of implementation using Java language.
授業の目的と到達目標
/Objectives and attainment
goals
At the end of the course the student should be able to:
- Understand the key advantages of concurrent and distributed systems and common problems the developers may encounter.
- Know different approaches to concurrent and distributed systems design, their advantages and disadvantages.
- Be able to implement simple concurrent and distributed systems using modern tools.
- Be aware of the historical perspective of the developments in this area, understand modern trends and technologies.
授業スケジュール
/Class schedule
1. Introduction
2. Basics of Concurrency
3. Synchronizing Processes
4. Introduction to Model Checking and Promela Language
5. Model Checking with SPIN and Linear Temporal Logic
6. From Shared-memory Model to Message Passing
7. Distributed Programming with MPI & Tuple Space Model
8. Types of Distributed Systems
9. Client-Server Programming
10. RMI: Distributed Objects in Java
11. Modern Concurrent Programming in Java
12. OpenMP Technology.
教科書
/Textbook(s)
- Distributed Systems: Principles and Paradigms by Andrew S. Tanenbaum and Maarten van Steen, Prentice Hall, 2007.

- M. Ben-Ari. Principles of Concurrent and Distributed Programming, 2nd Ed. Addison-Wesley, 2006.

- G. R. Andrews. Foundations of Multithreaded, Parallel, and Distributed Programming. Addision-Wesley, 2000.

- M. L. Liu, Distributed Computing: Principles and Applications, Addison-Wesley, 2004.
成績評価の方法・基準
/Grading method/criteria
The final grade is based on the following parts:

- Exercises (50% of the final score).
- Two exams (30% of the final score).
- Quizzes on lecture content (20% of the final score).

We keep strict deadlines in this course.
履修上の留意点
/Note for course registration
The presented course is not an introductory subject. It is intended for students who already have basic experience in programming such as Java Programming, Algorithms and Data Structures, Object-Oriented Programming, Operating Systems.


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3年
単位数
/Credits
3.0
責任者
/Coordinator
ラゲ ウダイ キラン
担当教員名
/Instructor
チュー ウォンミィング, ラゲ ウダイ キラン, 山田 竜平
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/01
授業の概要
/Course outline
Database Management System (DBMS, in short) is one of the fundamental courses in the field of computer science. In-depth knowledge on DBMS is critical to understand the advanced topics, such as data warehousing, data mining, artificial intelligence, big data analytics, cloud computing, machine learning, and deep learning.

In this course, the students will learn both theoretical and practical aspects of DBMS. The lecture classes facilitate the students to understand the theoretical concepts of DBMS. The exercise classes are intended to guide students in developing a real-world DBMS system.

Two periods of teaching + one period of exercise
授業の目的と到達目標
/Objectives and attainment
goals
Objective:
In this course, the students will understand the architecture of the database, types of databases, the methodology to design a database effectively, and the methods to query the database. The main goal of this class is to empower students with a skill set that can provide a competitive edge over others at their workplace.

Goals:
1. Enabling the students to choose an appropriate database system to satisfy their work requirements
2. Empowering students to develop their own database system
3. Facilitating students in developing cost-efficient queries

授業スケジュール
/Class schedule
Lecture topics:
1. Database System Concepts and Architecture
2. Data Modeling using Entity-Relationship Model and UML Modeling
3. Relational Algebra and Calculus
4. Structured Query Language-1 (Querying textual and numerical attributes)
5. Structured Query Language-2 (Querying spatial and temporal attributes)
6. Structured Query Language-3 (views, drivers, and other advanced topics)
7. MID-TERM EXAM
8. Functional Dependencies and Normalization
9. Data Storage and Indexing
10. Transaction Processing Concepts (ACID and BASE)
11. Database Recovery Techniques
12. Distributed Databases
13. Storing big data using HBASE-1
14. Storing big data using HBASE-2

Exercise topics:
1. Understanding SORAMAME air pollution data.
2. Developing ER-Schema for SORAMAME data
3. Developing SORAMAME database in Postgres (Part-1, with no spatiotemporal attributes)
4. Modifying SORAMAME database by incorporating spatiotemporal attributes
5. Adding the data into the base using JDBC drivers
6. Querying the data in Postgres -1
7. Querying the data in Postgres -2
8. Discussion on previous works
9. Optimizing SORAMAME database using Normalization
10. Creating views
11. Backup and restore of the SORAMAME database
12. Executing Spatial queries on the database
13. Visualizing spatial data using QGIS
教科書
/Textbook(s)
Fundamentals of database systems: ELMASRI and NAVATHE
成績評価の方法・基準
/Grading method/criteria
Mid-term: 15% of weightage
Classroom assignments: 10% of weightage
Final exam: 35% of weightage
Exercises: 40% of weightage


科目一覧へ戻る

開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
3年 , 4年
単位数
/Credits
3.0
責任者
/Coordinator
ラゲ ウダイ キラン
担当教員名
/Instructor
ラゲ ウダイ キラン, DANG Nam Khanh
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/01
授業の概要
/Course outline
Data Mining (DM) and Machine Learning (ML) are two sides of the same coin, which is knowledge discovery in data. DM puts the user at the center and focuses on providing an interpretable form of knowledge that exists in the data. On the contrary, ML puts accuracy at the center and focuses on black-box models to achieve high accuracy.

DM plays a crucial role in developing decision support systems or expert systems. In contrast, ML plays a pivotal role in recommender systems. An AI researcher should be familiar with both types of learning approaches.

Two periods of teaching + one period of exercise
授業の目的と到達目標
/Objectives and attainment
goals
Technological advances in the field of computer science have enabled organizations to collect voluminous data in databases. Useful knowledge that can empower end-users with competitive information is hidden in this voluminous data. Unfortunately, conventional statistical techniques are inadequate to extract knowledge hidden in this data. This scenario is called “Data Rich-Information Poor Situation” (or finding a needle in a haystack).  The field of data mining has emerged to address this problem in real-world applications.

Data mining represents a collection of techniques that aim to discover knowledge hidden in databases. Data mining techniques include pattern mining (or association rule mining), classification, clustering, and prediction.

In this course, the students will first understand the basic types of data and fundamental forms of the data. This understanding of data is critical to know the limitations of any data mining/machine learning technique. Next, students will learn about different data mining techniques. Finally, students will learn which data mining technique to apply to what type of data. The main goal of this class is to empower students with a skill set that can provide a competitive edge over others at their workplace.
授業スケジュール
/Class schedule
Lecture topics:
1. Introduction to Data Warehousing
2. Introduction to Data Mining
3. Frequent pattern mining
a. Apriori algorithm
b. ECLAT algorithm
4. Frequent pattern mining
a. Frequent pattern-growth
b. Rare Item Problem
5. Closed and maximal frequent pattern mining
6. Clustering techniques
a. Types of clustering techniques
b. K-means and Elbow method
7. Hierarchical clustering techniques
8. Density-based clustering techniques
9. Dimensionality reduction techniques
10. Classification techniques
a. Emerging and Jumping patterns
b. Associative classifiers
11. Classification techniques
a. Bayesian
b. Decision trees
12. Classification techniques
a. Lazy learners
13. Measures for evaluation
14. Prediction techniques

Exercise topics:
1. Assigning projects to students. Students have to code the algorithm presented in the paper.
2. Introduction to ARFF, WEKA, SPMF, and PAMI_Pykit
3. Executing Apriori and ECLAT algorithms on various databases and plots
4. Evaluating the FP-growth algorithm
5. Evaluation of Frequent pattern mining closed frequent pattern mining, and maximal frequent pattern mining algorithms
6. Clustering in WEKA - 1
7. Clustering in WEKA - 2
8. Discussion on project status
9. Dimensionality reduction using PCA
10. Classification in WEKA - 1
11. Classification in WEKA - 2
12. Classification in WEKA - 3
13. Presentation of the projects - 1
14. Presentation of the project - 2
教科書
/Textbook(s)
Data Warehousing and Mining: Han and Kamber
成績評価の方法・基準
/Grading method/criteria
Students will be graded based on the project and the final exam. The project carries 50% of weightage, 10% of weightage to classroom exercises, 25% of weightage of exercises, and the final exam has 15% of weightage.

In the project, a research article will be assigned to each student. Students have to write their own code for an algorithm presented in the report.


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