Seminar on Modern Software Engineering and Database Conceptson+Modern+Software... · To whom it may...

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Seminar on Modern Software Engineering andDatabase Concepts

Gunter Saake, Fabian Benduhn, David Broneske, GabrielCampero, Sandro Schulze

Arbeitsgruppe Datenbanken und Software Engineering

13. Oktober 2017

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Organisatorisches – Einordnung

Pro-Seminar, Bachelor, 3 CPDelivieries:

• 20 Min. Vortrag

• Bewertung 2 anderer Vortrage

Wissenschaftliches Seminar, Bachelor, 3CPDelivieries:

• 20 Min. Vortrag

• 5-8 Seiten Ausarbeitung

• Bewertung einer anderen Ausarbeitung

Saake et al. Seminar on Modern Software Engineering and Database Concepts 2

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Organisatorisches – Durchfuhrung

• Einfuhrungstermin (heute)

• Treffen mit einem Betreuer aus der Arbeitsgruppe (je nachThema)

• Vorlesungen zu wissenschaftlichem Schreiben undPrasentationen an Einzelterminen

• Vortrage als Blockseminar – Termin wird am Ende festgelegt

• Folien mussen eine Woche vor dem Vortrag eingereicht werden→ Feedback vom Betreuer

• Wissenschaftliche Ausarbeitung (nur Wiss. Sem.) muss 2Wochen vor dem Vortrag abgegeben werden.


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Was machen wir?

• Wichtige “Soft Skills“ oder auch Schlusselkompetenzenerlernen

• Vortragsweisen und -stil uben• “Konferenzflair“ erleben• Ein wissenschaftliches Papier schreiben• Arbeit mit entsprechenden Vorlagen (Empfehlung: LaTex)

• Einarbeitung in ein neues spannendes Thema

• Themen kommen aus aktueller Forschung→ Mogliches Thema fur Bachelor-Arbeit / Team-Projekt


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Vortrag

• 20 Minuten Vortrag

• 5-10 Minuten Diskussion/Fragen

• Uberziehen: Redner wird abgewurgt

• Zu Fruh: Mehr Fragen (ggf. mehr Kritik)

• Rechner wird gestellt, vor Veranstaltung Prasentationentesten!


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Vortrag: BewertungD

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Evaluationsbogen VortragArbeitsgruppe Datenbanken und Software Engineering

Titel des Vortrags:

Sehr Gut . . . Neutral . . . Schlecht

Prasentation Wertung: 1 2 3 4 5 6 7Auftreten, z.B.• Ausstrahlung, Dynamik• Motivationsfahigkeit, Uberzeugungskraft

Sprache und Stimme, z.B.• Lautstarke, Modulation, Klarheit• Sprechgeschwindigkeit, -flussigkeit

Mimik und Gestik, z.B.• Korperhaltung, Bewegungen• Ausdruck, Blickrichtung

Fachlicher Eindruck, z.B.• Kompetenz, Seriositat• Verbindlichkeit

Folien Wertung: 1 2 3 4 5 6 7Design, z.B.• Farben, Schriften, Formatierungen• Klarheit, Ubersichtlichkeit• Umfang des Inhalts pro Folie

Visualisierungen (Tabellen, Grafiken,...), z.B.• Erklarungskraft, Klarheit• Beschriftungen• Referenzkonzept

Vortragskonzept Wertung: 1 2 3 4 5 6 7Gliederung, z.B.• Aufteilung (Einleitung, Hauptteil, Schluss)• Die wichtigsten Punkte sind vorab klar

Argumentation, z.B.• Schlussigkeit, Roter Faden• Kompaktheit, Klarheit, Vollstandigkeit

Inhalt Wertung: 1 2 3 4 5 6 7Erlauterung der Rahmenbedingungen, z.B.• Hintergrund und Motivation (Begrundung)• Nutzen und Ziele (Bedeutung)

Erlauterung der Vorgehensweise, z.B.• Prinzip, Aufgaben, Ergebnisse• Probleme, Lessons Learned

Bemerkungen:


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Wissenschaftliches Papier


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Warum ein Papier schreiben?

Bekanntgeben von neuen Errungenschaften/Erfahrungen

• Publizieren = Ultimatives Ergebnis wissenschaftlicher Arbeit

• Forschung ist nie beendet, solange sie nicht publiziert wurde

Andere (z.B. Community) uber die eigene Arbeit informieren

• Anerkennung/Beachtung

• Kontakte, wertvolle Zusammen-/Mitarbeit

• Feedback

Fur euch:

→ Uben fur die Bachelor-Arbeit


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Paper: BewertungD

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Evaluationsbogen wissenschaftliche AusarbeitungArbeitsgruppe Datenbanken und Software Engineering

Titel der Ausarbeitung:

Autor:

Gutachter:

Sehr Gut . . . Neutral . . . Schlecht

Titel, Abstract, Einleitung Wertung: 1 2 3 4 5 6 7Diskussionspunkte (z.B.):

• Geeigneter Titel

• Qualitat der Zusammenfassung

• Hinreichende Motivation

• Klare Problemstellung

Struktur Wertung: 1 2 3 4 5 6 7Diskussionspunkte (z.B.):

• Roter Faden

• Sinnvolle Gliederung

• Geeignete Uberschriften zum Inhalt

Inhalt Wertung: 1 2 3 4 5 6 7Diskussionspunkte (z.B.):

• Ausreichende Grundlagen

• Keine unnotigen Informationen

• Referenzierung

• Plausible Begrundungen

• Klarheit der Vor-/Nachteile

• Gute/korrekte Nutzungvon Beispielen


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Themenvorstellung


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ThemenCPU “smaller than

”-selection

i n t pos = 0 ;f o r ( i n t i =0; i < a r r a y s i z e ; ++i ){

i f ( a r r a y [ i ] < comp va l )r e s u l t [ pos++]= i ;

}

GPU “smaller than”

-selectioni n t t i d = th r e a d I d x . x + b l o c k I d x . x * blockDim . x ;wh i l e ( t i d<a r r a y s i z e ){

b i tmask [ t i d ] = ( a r r a y [ t i d ] < compa r i s on va l u e ) ;t i d += blockDim . x * gr idDim . x ;

}

Code Optimizations (David Broneske)

1. B. Raducanu, P. Boncz, M. Zukowski. Micro Adaptivity inVectorwise. SIGMOD, 2013.

2. K. Datta, M. Murphy, V. Volkov, et al.: Stencil ComputationOptimization and Auto-tuning on State-of-the-Art MulticoreArchitectures. SC, 2008.


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SIMD Acceleration for Index Structures (David Broneske)

1. S. Zeuch, J. Freytag, F. Huber: Adapting Tree Structures forProcessing with SIMD Instructions. EDBT, 2013.

2. C. Kim, J. Chhugani, N. Satish, et al. FAST: FastArchitecture Sensitive Tree Search on Modern CPUs andGPUs. SIGMOD, 2010.


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Why source code refactorings are (not) used (SandroSchulze) Refactoring is a very well-known concept that aims atimproving the source code (i.e., its structure) while preserving itsbehavior. Many approaches have been proposed and even morestudies on the effect of refactorings have been made, but still thereis no common agreement when to refactor, what are possiblebenefits, and why people do not refactor. In this topic, the task isto review papers that focus on the aspect of refactoring applicationand impact. In particular, it is of interest to identify possiblereasons in favor and against refactoring, but also to identifypossible conflicting arguments and results among these papers.


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ThemenWhy source code refactorings are (not) used (SandroSchulze)

1. Kim, M., Zimmermann, T., & Nagappan, N. (2012,November). A field study of refactoring challenges andbenefits.

2. Murphy-Hill, E., & Black, A. P. (2008, May). Breaking thebarriers to successful refactoring: observations and tools forextract method.

3. Moser, R., Abrahamsson, P., Pedrycz, W., Sillitti, A., & Succi,G. (2008). A case study on the impact of refactoring onquality and productivity in an agile team.


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To whom it may concern: reasons for zombi bug reports(Sandro Schulze) Bug databases are an important instrument,especially in open-source software (OSS), to track reported bugsand systematically remove them. However, open bug databasestend to being piled with hundreds of bug reports, which are hard tomanage in time. Thus, bug reports may be a) wrongly classified, b)wrongly assigned, or c) not considered at all. The task of this topicis to review literature that adresses one ore more of the aboveproblems. As a result, a systematic survey should be done,resulting into a comprehensive classification of why and when bugreports are doomed to not being resolved.


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To whom it may concern: reasons for zombi bug reports(Sandro Schulze)

1. Guo, P. J., Zimmermann, T., Nagappan, N., & Murphy, B.(2011, March). Not my bug! and other reasons for softwarebug report reassignments.

2. D’Ambros, M., Lanza, M., & Pinzger, M. (2007, June).A Bug’s Life”Visualizing a Bug Database.

3. Anvik, J., Hiew, L., & Murphy, G. C. (2005, October). Copingwith an open bug repository.


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Variability in Non-ACID Transactions (Fabian Benduhn)

1. Gallina, Barbara, and Nicolas Guelfi. SSPLACID: AnSPL-oriented, ACTA-based, language for reusing (Varying)ACID properties.SSoftware Engineering Workshop, 2008.SEW’08. 32nd Annual IEEE. IEEE, 2008.

2. Gallina, Barbara, and Nicolas Guelfi. ”Reusing transactionmodels for dependable cloud computing.SSoftware Reuse inthe Emerging Cloud Computing Era (2012): 248-277.

3. (Levy, Korth, & Silberschatz, 1991)


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Reconfiguration in Dynamic Software Product Lines(FabianBenduhn)

1. Representation of configuration state

2. Reconfiguration mechanisms

3. Analyses/Metrics


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ThemenReconfiguration in Dynamic Software Product Lines(FabianBenduhn)

1. Gomaa, Hassan, and Mohamed Hussein. ”Dynamic softwarereconfiguration in software product families.InternationalWorkshop on Software Product-Family Engineering. Springer,Berlin, Heidelberg, 2003.

2. Bencomo, Nelly, et al. ”Dynamically Adaptive Systems areProduct Lines too: Using Model-Driven Techniques to CaptureDynamic Variability of Adaptive Systems.SSPLC (2). 2008.

3. Capilla, Rafael, et al. An overview of Dynamic SoftwareProduct Line architectures and techniques: Observations fromresearch and industry.”Journal of Systems and Software 91(2014): 3-23.


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ThemenAgile Formal Methods (Fabian Benduhn)

1. Larsen, Peter Gorm, John S. Fitzgerald, and Sune Wolff. AreFormal Methods Ready for Agility? A Reality Check. FM+AM. 2010.

2. Black, Sue, et al. Formal versus agile: Survival of the fittest.Computer 42.9 (2009).

3. Olszewska, Marta, Sergey Ostroumov, and Marina Walden.Synergising Event-B and Scrum-Experimentation on a FormalDevelopment in an Agile Setting. Abo Akademi University,Tech. Rep 1152 (2016).

4. Edmunds, Andrew, Marta Olszewska, and Marina Walden.Using the Event-B formal method for disciplined agile deliveryof safety-critical systems. (2016).


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ThemenVerification of Abstract State Machines (Fabian Benduhn)

1. Farahbod, Roozbeh, Uwe Glasser, and George Ma. Modelchecking coreasm specifications.”Proceedings of the 14thInternational ASM Workshop (ASM’07). 2007.

2. Arcaini, Paolo, Angelo Gargantini, and Elvinia Riccobene.AsmetaSMV: a way to link high-level ASM models tolow-level NuSMV specifications. Abstract State Machines,Alloy, B and Z (2010): 61-74.

3. Ouimet, Martin, and Kristina Lundqvist. The TASM toolset:Specification, simulation, and formal verification of real-timesystems. 2007.


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Towards Evolutionary Database Components- State of theart and Future Directions (Gabriel Campero)

• Traditional query engines: 1. analyze 2. optimize, 3. execute

• Challenge:• Unreliable estimates

• Concurrent processing of requests• Very complex queries• Changing data/platform/workload characteristics

• Solution: Adaptive Query Processing• runtime feedback• tuning of query plan and processing


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Towards Evolutionary Database Components- State of theart and Future Directions(Gabriel Campero)

• Generalization: Towards Evolutionary Features (GrandChallenge)

• Task: Identify connections regarding state-of-the-art, discussopportunities etc.

• • Alagiannis, Ioannis, Stratos Idreos, and Anastasia Ailamaki.”H2O: a hands-free adaptive store.”2014.

• Idreos, Stratos, Lukas M. Maas, and Mike S. Kester.Evolutionary Data Systems.”2017. Back, Thomas.

• Evolutionary algorithms in theory and practice: evolutionstrategies, evolutionary programming, genetic algorithms. 1996.


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ThemenTemporal Graph Analysis (Gabriel Campero)

• Moffitt, Vera Zaychik, and Julia Stoyanovich. ”Towards adistributed infrastructure for evolving graph analytics.”2016.

• Semertzidis, Konstantinos, and Evaggelia Pitoura. ”HistoricalTraversals in Native Graph Databases.In Advances inDatabases and Information Systems, pp. 167-181. Springer,Cham, 2017.

• Campero Durand, Gabriel, Marcus Pinnecke, David Broneske,and Gunter Saake. ”Backlogs and Interval Timestamps:Building Blocks for Supporting Temporal Queries in GraphDatabases.”2017.

• Then, Manuel, Timo Kersten, Stephan Gunnemann, AlfonsKemper, and Thomas Neumann. Automatic algorithmtransformation for efficient multi-snapshot analytics ontemporal graphs.”2017


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ThemenThe 20 Queries Rule in Practice: Current Data

Management Support for Data Science and Request forFeatures (Gabriel Campero)

• In this project we seek to understand the current state of datamanagement support for data science by designing andcarrying out a survey among researchers in our University. Inthis survey a special consideration will be given to dataexploration. Furthermore, we seek to answer if the mostcommonly used tools follow Jim Gray’s 20 queries rule (i.e.start the design with the most important 20 queries)individually, or if this is only achieved by integration withother tools. Finally we propose to collect requests for features,which might inform the database community abouttechnologies that need to be developed.


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ThemenThe 20 Queries Rule in Practice: Current Data

Management Support for Data Science and Request forFeatures (Gabriel Campero)

• Szalay, Alexander S., and Jose A. Blakeley. ”Gray’s laws:database-centric computing in science.”(2009): 5-11.

• Cao, Longbing. ”Data science: Challenges anddirections.”Communications of the ACM 60, no. 8 (2017):59-68.


• Then, Manuel, Timo Kersten, Stephan Gunnemann, AlfonsKemper, and Thomas Neumann. Automatic algorithmtransformation for efficient multi-snapshot analytics ontemporal graphs.”2017

• Mottin, Davide, Matteo Lissandrini, Yannis Velegrakis, andThemis Palpanas. ”New trends on exploratory methods fordata analytics.”Proceedings of the VLDB Endowment 10, no.12 (2017): 1977-1980.


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Performance Factors for Large Scale Machine LearningApplications (Gabriel Campero)

• Introduction to the field (with a focus on deep learning),

• Listing the most used tools for large scale machine learning.

• A brief recapitulation on existing challenges in managing suchmodels

• A discussion on the components of such architecture (i.e., thetrainer, the model validator, etc.) and runtime performancefactors related to them.


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ThemenPerformance Factors for Large Scale Machine Learning

Applications (Gabriel Campero)

• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. ”Deeplearning.”Nature 521, no. 7553 (2015)

• Sculley, D., Todd Phillips, Dietmar Ebner, Vinay Chaudhary,and Michael Young. ”Machine learning: The high-interestcredit card of technical debt.”(2014).


• Baylor, Denis, Eric Breck, Heng-Tze Cheng, Noah Fiedel,Chuan Yu Foo, Zakaria Haque, Salem Haykal et al. ”TFX: ATensorFlow-Based Production-Scale Machine LearningPlatform.In Proceedings of the 23rd ACM SIGKDDInternational Conference on Knowledge Discovery and DataMining, pp. 1387-1395. ACM, 2017.1977-1980.


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Nachste Schritte

• Anmeldung bis 18.10.2017 mit Name, Matrikelnr,Themenwunsch an [email protected]

• Themenvergabe

• Terminfindung


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