Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    Belépés
    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Software Development Laboratory 1

    A tantárgy neve magyarul / Name of the subject in Hungarian: Szoftverfejlesztés laboratórium 1

    Last updated: 2019. december 3.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Mérnökinformatikus szak

    BSc képzés

    Szoftverfejlesztés specializáció
    Course ID Semester Assessment Credit Tantárgyfélév
    VIAUAC09   0/0/2/f 3  
    3. Course coordinator and department Dr. Dudás Ákos,
    4. Instructors

    Név:

    Beosztás:

    Tanszék, Int.:

     

    Dr. Dudás Ákos

    docens

    Automatizálási és Alkalmazott Informatikai Tanszék

    5. Required knowledge Data-driven systems, Software technologies, Software laboratory 3, Softwaretechniques.
    6. Pre-requisites
    Kötelező:
    (((Szakirany("AMINszoftfejlAUT", _) VAGY
    Szakirany("AMINszoftfejlIIT", _) VAGY
    Szakirany("AMINszoftfejlMIT", _) )

    ÉS TargyEredmeny( "BMEVIAUAC01" , "jegy" , _ ) >= 2 )



    VAGY Szakirany("AMIszoft", _)

    VAGY Szakirany("AMIrendszfejl", _)


    VAGY Training.code=("5NAA8") )


    ÉS NEM ( TárgyEredmény( "BMEVIAUA372" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIAUA372", "FELVETEL", AktualisFelev()) > 0
    VAGY
    TárgyEredmény( "BMEVIIIA374" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIIIA374", "FELVETEL", AktualisFelev()) > 0
    VAGY
    TárgyEredmény( "BMEVIAUAC03" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIAUAC03", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rendek grafikus formában itt láthatók.

    Ajánlott:

    Recommended courses: Software techniques, Databases

    7. Objectives, learning outcomes and obtained knowledge

    The goal of the course is to practice the material learned during Data-driven systems through laboratory exercises.

    8. Synopsis

    -        Microsoft SQL Server programming (platform dependent SQL queries, triggers, cursors, stored procedures)

    -        MongoDB database platform programming (data access methods, client-side data access using C# language, atomic data modifications, aggregation pipelines)

    -        Query optimization, usage of indices (CI, NCI, TS, CISC, CISE, NCISE, NSISC, NLJ algorithms comparison, performance evaluation)

    -        Programming Entity Framework (Entity Data Model, query language)

    -        SQL Reporting Services (basics of Reporting Services, data sources, datasets, tables, reports, formatting, diagrams)

    -        Developing multi-tier applications using REST (REST-based service development in C# using Visual Studio)

    9. Method of instruction

    Exercises are to be solved during a computer laboratory class. The classes focus mainly on individual work. The exercises define a framework in which the tasks should be completed (such as, starter source code). The solutions must be submitted electronically. The solutions are software source codes and/or documentation, which the exercises clearly define.

     

    The laboratories can be of two type.

    • Standard computer laboratory class with a handout and aids describing the exercises. The students mainly work alone, but there is an instructor available for questions. This type of laboratory is used for the topics and technologies where the presence of a laboratory instructor is important.
    • Certain topics are suitable for completion and submission from at home. For these, the assigned time slot of the course is used for consultation option. Attendance of the consultation may require preliminary registration so that the required number of instructors can be provided. The following topics are available for this type of laboratory:

    o   MongoDB database platform programming

    o   Programming Entity Framework

    10. Assessment

    a. During the semester:

     

    - The student must prepare for the laboratories based on the materials of the exercises and the related materials of course Data-driven systems.

    - For laboratories that require attendance, the student must arrive to the laboratory class on time and must participate in the laboratory class as the instructor and the exercise handout specifies it.

    - For the laboratories that do not require attendance, the requirement is the completion of the exercises based on the laboratory handouts. During the completion of these laboratories, to ensure that the student did the work herself, the instructor may specify additional requirements that prove this fact. Such requirement can be, for example, the documentation of the work process through computer screenshots; frequent commits to a Git repository showing the incremental work process; or similar techniques. If the submission of the student does not adhere to these requirements, the instructor can require the student to participate in an additional class, where attendance is required, and the student must complete exercises similar to the work at home, but this time, supervised by instructors in person.

    - Any material produced during the laboratory (including source code, documentation, etc., as specified by the instructor) shall be submitted to the instructor as requested. The exact deadline of submission can be specified by the instructor.

    - Each laboratory is graded separately. The final grade is the average of the individual grades. Any laboratory that was not completed is counted with grade 1.  

     

    b. During the exam period:

     None.

    11. Recaps

    The laboratory classes where attendance is required may be repeated upon preliminary request, depending on room capacity. Materials not submitted until the deadline may not be handed in late.

    12. Consultations

    Available upon request to the laboratory instructor.

    13. References, textbooks and resources

    Course materials and exercises.

    14. Required learning hours and assignment

    Kontakt óra

    28

    Félévközi készülés órákra

    28

    Jegyzőkönyv elkészítése

    34

    Összesen

    90

    15. Syllabus prepared by

    Név:

    Beosztás:

    Tanszék, Int.:

    Dr. Dudás Ákos

    adjunktus

    Automatizálási és Alkalmazott Informatikai Tanszék

    Dr. Kővári Bence

    docens

    Automatizálási és Alkalmazott Informatikai Tanszék

    Dr Mezei Gergely

    docens

    Automatizálási és Alkalmazott Informatikai Tanszék

    Simon Gábor

    mérnöktanár

    Automatizálási és Alkalmazott Informatikai Tanszék