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Lesegruppe Software-Technik (24125)

Semester: Sommersemester 2022
LP (ECTS): 1
SWS: 1
Studiengang: Master Informatics, Master Information Engineering
Ansprechpartner: Dr.-Ing. Erik Burger
Ort und Zeit der Lehrveranstaltung
Mittwoch 11:30–12:30
Hybrid
50.34, 348 / Microsoft Teams (siehe Beschreibung)
ILIAS-Bereich
Microsoft Teams

https://campus.studium.kit.edu/ev/r5H3jHBdTLazbN 9LILKyQ/de/conversations?groupId=a0cbe15d-dc9a-4162-8796-3417d585dcae&tenantId=4f5eec75-46fd-43f8-8d24-62bebd9771e5

Seite im Vorlesungsverzeichnis
https://campus.studium.kit.edu/events/catalog.php#!campus/all/event.asp?gguid=0x99BE89244849462C9B77C79DAF7605FF

In der Lesegruppe werden zweiwöchentlich wissenschaftliche Publikationen anderer Forschergruppen vorgestellt und diskutiert. Die Veranstaltung dient der Herstellung eines gemeinsamen Wissensstandes und des Austausches von Doktoranden/-innen und Betreuer/-innen. Jede/-r Teilnehmer/-in kann (und sollte) eigene Vorschläge für zu besprechende Publikationen einbringen.

Einladungslink zu Microsoft Teams

Sie können dem Team mit folgendem Link beitreten: http://connect.studium.kit.edu/teams/join/i2zwI8R58J

Link zum Team: https://teams.microsoft.com/l/team/19%3aede66f482bd94928b260e22292b8d41d%40thread.tacv2/conversations?groupId=a0cbe15d-dc9a-4162-8796-3417d585dcae&tenantId=4f5eec75-46fd-43f8-8d24-62bebd9771e5

Allgemeine Informationen

Ziel der Lesegruppe ist es, innerhalb der Gruppe einen gemeinsamen Wissenstand zu schaffen, sich bei Verständnisfragen gegenseitig zu unterstützen und einen Rahmen für organisierte und fokussierte Fachdiskussionen zu geben. Die Lesegruppe ist auch eine Hilfe für Studierende, um verwandte Arbeiten bei Abschlussarbeiten einzuordnen und zu bewerten. Hier können die Studierenden gelesene oder verwandte Publikationen vorstellen und Feedback bekommen.

Die Teilnahme interessierter Studierender ist explizit erwünscht, aber auch alle anderen Interessenten/-innen sind herzlich willkommen. Keine Angst: Die Inhalte der Publikationen werden nicht "abgefragt", müssen vorher nicht verstanden und auch nicht zwingend gelesen worden sein. Die wichtigste Einsicht in der Lesegruppe ist es, zu lernen, wie Publikationen kritisch gelesen werden, und worauf es dabei ankommt.

Der Ablauf ist immer wie folgt: In der Ankündigung (per E-Mail und hier auf der Wiki-Seite) wird der zu lesende Artikel veröffenlticht. In der Lesegruppe selbst wird der Artikel (oder in bei sehr verwandten Artikeln auch mehrere) vorgestellt (mit Fokus auf die wichtigen Stellen) und der Artikel und dazu offene Fragen diskutiert.

Das ganze soll für alle eine Veranstaltung mit geringem Mehraufwand sein:

  • Nicht alle Teilnehmer müssen die relevanten Artikel bis auf das letzte Bit kennen und der Vortragende darf auch durchaus selbst offene Fragen beisteuern.
  • Die Vortragenden sollten die Vortragshinweise zur Strukturierung des Vortrags nutzen.

Leistungspunkte

Die Lesegruppe kann als Lehrveranstaltung mit einem ECTS-Punkt im Bereich "Überfachliche Qualifikationen" angerechnet werden. Um den Leistungspunkt zu erhalten, gibt es zwei Voraussetzungen, die beide erfüllt sein müssen:

  • Teilnahme an der Lesegruppe über ein Semester (entsprechend länger, wenn nicht alle Termine regelmäßig besucht werden oder die Veranstaltung zu selten stattfindet).
  • Das Vorstellen einer Publikation. Dieser Beitrag und die Diskussionsbeiträge gelten als Grundlage für die Leistungsbewertung.
  • Die Lesegruppe ist eine unbenotete Studienleistung.
  • Die Lesegruppe kann leider nicht für das Bachelor-Studium angerechnet werden.

Dennoch wird, gerade in Anbetracht der Vielfalt von Themen, die regelmäßige Teilnahme an Diskussionen nicht verlangt. Wer nur in aktuelle Themen hineinschnuppern möchte ohne den Leistungspunkt erhalten zu wollen, ist auch ohne eigene Beiträge jederzeit willkommen.

Meldet euch bei Interesse und Fragen beim Lesegruppen-Beauftragten, um die Details zu klären. Weitere Informationen können ebenfalls dem Modulhandbuch entnommen werden.

Termine

Die Lesegruppe findet in der Regel in ungeraden Kalenderwochen statt und beginnt in der zweiten Vorlesungswoche.

Kommende Termine

Mittwoch, 13. Juli 2022, 11:30–12:30 Uhr (Hybrid)
Vortragende-/r Daniel Zimmermann
Forschungsgruppe
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PDF
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Mittwoch, 27. Juli 2022, 11:30–12:30 Uhr (Hybrid)
Vortragende-/r Kai Marquardt
Forschungsgruppe MCSE
Titel
Autoren
PDF
URL
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Um einen neuen Termin anzulegen, bitte das Datum hier eintragen:

 

Vergangene Termine

Mittwoch, 29. Juni 2022, 11:30–12:30 Uhr (Hybrid)
Vortragende-/r Sophie Schulz
Forschungsgruppe MCSE
Titel A comparison of security requirements engineering methods
Autoren Benjamin Fabian, Seda Gürses, Maritta Heisel, Thomas Santen & Holger Schmidt
PDF https://link.springer.com/content/pdf/10.1007/s00766-009-0092-x.pdf
URL https://link.springer.com/article/10.1007/s00766-009-0092-x
BibTeX https://dblp.org/rec/journals/re/FabianGHSS10.html?view=bibtex
Abstract This paper presents a conceptual framework for security engineering, with a strong focus on security requirements elicitation and analysis. This conceptual framework establishes a clear-cut vocabulary and makes explicit the interrelations between the different concepts and notions used in security engineering. Further, we apply our conceptual framework to compare and evaluate current security requirements engineering approaches, such as the Common Criteria, Secure Tropos, SREP, MSRA, as well as methods based on UML and problem frames. We review these methods and assess them according to different criteria, such as the general approach and scope of the method, its validation, and quality assurance capabilities. Finally, we discuss how these methods are related to the conceptual framework and to one another.
Mittwoch, 15. Juni 2022, 12:00–13:00 Uhr (50.34, 333)
Vortragende-/r Yves Kirschner
Forschungsgruppe ARE
Titel A Comparative Analysis of Software Architecture Recovery Techniques
Autoren Joshua Garcia, Igor Ivkovic, and Nenad Medvidovic
PDF https://ieeexplore.ieee.org/iel7/6684409/6693054/06693106.pdf
URL https://doi.org/10.1109/ASE.2013.6693106
BibTeX https://dblp.org/rec/conf/kbse/GarciaIM13.bib
Abstract Many automated techniques of varying accuracy have been developed to help recover the architecture of a software system from its implementation. However, rigorously assessing these techniques has been hampered by the lack of architectural “ground truths”. Over the past several years, we have collected a set of eight architectures that have been recovered from open-source systems and independently, carefully verified. In this paper, we use these architectures as ground truths in performing a comparative analysis of six state-of-the-art software architecture recovery techniques. We use a number of metrics to assess each technique for its ability to identify a system's architectural components and overall architectural structure. Our results suggest that two of the techniques routinely outperform the rest, but even the best of the lot has surprisingly low accuracy. Based on the empirical data, we identify several avenues of future research in software architecture recovery.
Mittwoch, 1. Juni 2022, 00:00–00:00 Uhr (Hybrid)
Vortragende-/r Dieser Termin fällt aus!
Forschungsgruppe
Titel
Autoren
PDF
URL
BibTeX
Abstract
Mittwoch, 18. Mai 2022, 12:00–00:00 Uhr ()
Vortragende-/r Dieser Termin fällt aus!
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Titel
Autoren
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URL
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Mittwoch, 4. Mai 2022, 00:00–00:00 Uhr (Hybrid)
Vortragende-/r Hamideh Hajiabadi
Forschungsgruppe MCSE
Titel Noise2Atom: unsupervised denoising for scanning transmission electron microscopy images
Autoren Feng Wang, Trond R. Henninen, Debora Keller & Rolf Erni
PDF https://appmicro.springeropen.com/articles/10.1186/s42649-020-00041-8
URL https://appmicro.springeropen.com/articles/10.1186/s42649-020-00041-8
BibTeX
Abstract We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain S to a target domain C, where S is for our noisy experimental dataset, and C is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.
Mittwoch, 20. April 2022, 12:00–13:00 Uhr (MS Teams)
Vortragende-/r Larissa Schmid
Forschungsgruppe MCSE
Titel On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support
Autoren Miguel Velez, Pooyan Jamshidi, Norbert Siegmund, Sven Apel, Christian Kästner
PDF https://arxiv.org/pdf/2203.10356.pdf
URL https://arxiv.org/abs/2203.10356
BibTeX
Abstract Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can support developers in this task, there is limited empirical evidence of how useful the techniques are to address the actual needs that developers have when debugging the performance of configurable software systems; most techniques are often evaluated in terms of technical accuracy instead of their usability. In this paper, we take a human-centered approach to identify, design, implement, and evaluate a solution to support developers in the process of debugging the performance of configurable software systems. We first conduct an exploratory study with 19 developers to identify the information needs that developers have during this process. Subsequently, we design and implement a tailored tool, adapting techniques from prior work, to support those needs. Two user studies, with a total of 20 developers, validate and confirm that the information that we provide helps developers debug the performance of configurable software systems.
Mittwoch, 9. Februar 2022, 12:00–13:00 Uhr ((MS Teams))
Vortragende-/r Martina Rapp
Forschungsgruppe AbQP
Titel Applying Machine Learning in Self-adaptive Systems: A Systematic Literature Review
Autoren Omid Gheibi, Danny Weyns, Federico Quin
PDF https://dl.acm.org/doi/pdf/10.1145/3469440
URL https://dl.acm.org/doi/10.1145/3469440
BibTeX https://dblp.org/rec/journals/taas/GheibiWQ20.html?view=bibtex
Abstract Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.
Mittwoch, 26. Januar 2022, 12:00–13:00 Uhr (MS Teams)
Vortragende-/r Max Scheerer
Forschungsgruppe AbQP
Titel The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms
Autoren Marie Salm, Johanna Barzen, Uwe Breitenbücher, Frank Leymann, Benjamin Weder, Karoline Wild
PDF https://www.iaas.uni-stuttgart.de/publications/Salm2020 NISQAnalyzer.pdf
URL https://www.springerprofessional.de/the-nisq-analyzer-automating-the-selection-of-quantum-computers-/18660564
BibTeX https://dblp.org/rec/conf/summersoc/SalmBBLWW20.html?view=bibtex
Abstract Quantum computing can enable a variety of breakthroughs in research and industry in the future. Although some quantum algorithms already exist that show a theoretical speedup compared to the best known classical algorithms, the implementation and execution of these algorithms come with several challenges. The input data determines, for example, the required number of qubits and gates of a quantum algorithm. A quantum algorithm implementation also depends on the used Software Development Kit which restricts the set of usable quantum computers. Because of the limited capabilities of current quantum computers, choosing an appropriate one to execute a certain implementation for a given input is a di cult challenge that requires immense mathematical knowledge about the implemented quantum algorithm as well as technical knowledge about the used Software Development Kits. In this paper, we present a concept for the automated analysis and selection of implementations of quantum algorithms and appropriate quantum computers that can execute a selected implementation with a certain input data. The practical feasibility of the concept is demonstrated by the prototypical implementation of a tool that we call NISQ Analyzer.
Mittwoch, 12. Januar 2022, 12:00–13:00 Uhr (Teams)
Vortragende-/r Tobias Hey
Forschungsgruppe
Titel Information retrieval versus deep learning approaches for generating traceability links in bilingual projects
Autoren Jinfeng Lin, Yalin Liu, Jane Cleland-Huang
PDF https://link.springer.com/content/pdf/10.1007/s10664-021-10050-0.pdf
URL https://doi.org/10.1007/s10664-021-10050-0
BibTeX https://citation-needed.springer.com/v2/references/10.1007/s10664-021-10050-0?format=bibtex&flavour=citation
Abstract Software traceability links are established between diverse artifacts of the software development process in order to support tasks such as compliance analysis, safety assurance, and requirements validation. However, practice has shown that it is difficult and costly to create and maintain trace links in non-trivially sized projects. For this reason, many researchers have proposed and evaluated automated approaches based on information retrieval and deep-learning. Generating trace links automatically can also be challenging – especially in multi-national projects which include artifacts written in multiple languages. The intermingled language use can reduce the efficiency of automated tracing solutions. In this work, we analyze patterns of intermingled language that we observed in several different projects, and then comparatively evaluate different tracing algorithms. These include Information Retrieval techniques, such as the Vector Space Model (VSM), Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and various models that combine mono- and cross-lingual word embeddings with the Generative Vector Space Model (GVSM), and a deep-learning approach based on a BERT language model. Our experimental analysis of trace links generated for 14 Chinese-English projects indicates that our MultiLingual Trace-BERT approach performed best in large projects with close to 2-times the accuracy of the best IR approach, while the IR-based GVSM with neural machine translation and a monolingual word embedding performed best on small projects.
Mittwoch, 15. Dezember 2021, 12:00–13:00 Uhr (Teams)
Vortragende-/r Dieser Termin fällt aus!
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PDF
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… weitere Ergebnisse (seit 2018)

Termine vor 2018 sind im Archiv zu finden.

Vorgeschlagene Artikel

Jeder, der einen interessanten Artikel in der Lesegruppe vorstellen möchte, kann sich hier eintragen (bitte eigenen Namen nicht vergessen!) oder eine Email an Emre Taspolatoglu or Manar Mazkatli schreiben.

Mögliche Papers:

Mögliche Kandidaten:

  • Performance Evaluation of Component-based Software Systems: A Survey – Koziolek [PDF fulltext] [BibTeX]
  • A Flexible Infrastructure for Multi Level Language Engineering – Atkinson, Gutheil, Kennel [www]
  • Foundations for the study of software architecture – Perry, Wolf [www]
  • Defect Frequency and Design Patterns: An Empirical Study of Industrial Code – Vokac [www]
  • Aligning Organizations Through Measurement - The GQM+Strategies Approach - Basili, Trendowicz, Kowalczyk, Heidrich, Seaman, Münch, Rombach [doi]
  • Object-Oriented Modeling with Adora - Glinz, Berner, Joos [www]
  • Software Aging - Parnas [www]
  • Practical relevance of software engineering research: synthesizing the community’s voice - Garousi, Borg, Oivo www
  • Weitere bitte hier eintragen

Studenten, die nach einer guten Publikation suchen, die sie in der Lesegruppe vorstellen können, seien auf folgende Standardpublikationen hingewiesen:

  • The Past, Present, and Future of Software Architecture - Kruchten, Obbink, Stafford [PDF fulltext] [BibTeX]
    Viele der in diesem Einführungsartikel genannten Publikationen stellen Meilensteine der Softwaretechnik dar, es eignen sich fast alle für die Vorstellung in der Lesegruppe.

Organisation (intern)