Semantische Suche
Mittwoch, 8. November 2023, 15:30 Uhr
iCal (Download)
Ort: Raum 333 (Gebäude 50.34)
Webkonferenz: https://sdq.kastel.kit.edu/wiki/SDQ-Institutsseminar/Microsoft_Teams
| Vortragende(r) | Jiangang Huang |
|---|---|
| Titel | Evaluation of a Reverse Engineering Approach in the Context of Component-Based Software Systems |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Yves Kirschner |
| Vortragsmodus | in Präsenz |
| Kurzfassung | This thesis aims to evaluate the component architecture generated by component-based software systems after reverse engineering. The evaluation method involves performing a manual analysis of the respective software systems and then comparing the component architecture obtained through the manual analysis with the results of reverse engineering. The goal is to evaluate a number of parameters, with a focus on correctness, related to the results of reverse engineering. This thesis presents the specific steps and considerations involved in manual analysis. It will also perform manual analysis on selected software systems that have already undergone reverse engineering analysis and compare the results to evaluate the differences between reverse engineering and ground truth. In summary, this paper evaluates the accuracy of reverse engineering by contrasting manual analysis with reverse engineering in the analysis of software systems, and provides some direction and support for the future development of reverse engineering. |
Freitag, 17. November 2023, 11:30 Uhr
iCal (Download)
Ort: Raum 010 (Gebäude 50.34)
Webkonferenz: https://sdq.kastel.kit.edu/institutsseminar/Microsoft_Teams
| Vortragende(r) | Dennis Steinbuch |
|---|---|
| Titel | Ein Ansatz zur Wiederherstellung von Nachverfolgbarkeitsverbindungen für natürlichsprachliche Softwaredokumentation und Quelltext |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Dominik Fuchß |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Wartbarkeit spielt eine zentrale Rolle für die Langlebigkeit von Softwareprojekten. Ein wichtiger Teil der Wartbarkeit besteht darin, dass die natürlichsprachliche Dokumentation des Quelltextes einen guten Einblick in das Projekt und seinen dazugehörigen Quelltext liefert. Zur besseren Wartbarkeit dieser beiden Software-Artefakte besteht die Aufgabe dieser Arbeit darin, Verbindungen zwischen den Elementen dieser beiden Artefakte aufzubauen. Diese Verbindungen heißen Trace Links und können für verschiedene Zwecke der Wartbarkeit genutzt werden. Diese Trace Links ermöglichen zum Beispiel die Inkonsistenzerkennung zwischen den beiden Software-Artefakten oder können auch für verschiedene Analysen benutzt werden. Um diese Trace Links nachträglich aus den beiden Software-Artefakten natürlichsprachlicher Dokumentation und Quelltext zu gewinnen, wird das bereits bestehende ArDoCo Framework benutzt und auf das Software-Artefakt Quelltext erweitert. Ebenfalls werden ArDoCos bestehende Entscheidungskriterien auf den neuen Kontext angepasst. Der neuartige Kontext führt zu Herausforderungen bezüglich der Datenmenge, die durch neue Entscheidungskriterien adressiert werden. Dabei zeugen die Ergebnisse dieser Arbeit eindeutige von Potenzial, weswegen weiter darauf aufgebaut werden sollte. |
| Vortragende(r) | Fabian Reinbold |
|---|---|
| Titel | Entity Recognition in Software Documentation Using Trace Links to Informal Diagrams |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Dominik Fuchß |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Natural Language Software Architecture Documentation ( NLSAD ) and Software Architecture Model ( SAM) provide information about a software systems design and qualities. Inconsistencies between these artifacts can negatively impact the comprehension and evolution of the system. ArDoCo is an approach that was proposed in prior work by Keim et al. to find such inconsistencies and relies on Traceability Link Recovery (TLR) between entities in the NLSAD and SAM . ArDoCo searches for Unmentioned Model Elements (UMEs) in the model and Missing Model Elements (MMEs) in the text using the linkage information. ArDoCo’s approach shows promising results but has room for improvement regarding precision due to falsely identified textual entities. This work proposes using informal diagrams from the Software Architecture Documentation (SAD) to improve this. The approach performs an additional TLR between the textual entities and the diagram entities. According to heuristics, the linkage of textual entities and diagram entities is utilized to increase or decrease the confidence in textual entities. The Diagram Text TLR and its impact on ArDoCo’s performance are evaluated separately using the same data set as previous work by Keim et al. The data set was extended to include informal diagrams. The Diagram Text TLR achieves a good F1-score with Optical Character Recognition (OCR) of 0.54. The approach improves the MME detection (0.77→0.94 accuracy) by lowering the amount of falsely identified textual entities (0.39→0.69 precision) with a negligible impact on recall. The UME detection and ArDoCo ’s NLSAD to SAM are slightly positively impacted and continue to perform excellently. The results show that using informal diagrams to improve entity recognition in the text is promising. Room for improvement exists in dealing with issues related to OCR and diagram element processing. |
| Vortragende(r) | Jianan Ye |
|---|---|
| Titel | Traceability Link Recovery for Relations in Natural Language Software Architecture Documentation and Software Architecture Models |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Dominik Fuchß |
| Vortragsmodus | in Präsenz |
| Kurzfassung | In software development, software architecture plays a vital role in developing and maintaining software systems. It is communicated through artifacts such as software architecture documentation (SAD) and software architecture models (SAM). However, maintaining consistency and traceability between these artifacts can be challenging. If there are inconsistencies or missing links, it can lead to errors, misunderstandings, and increased maintenance costs. This thesis proposes an approach for recovering traceability links of software architecture relations between natural language SAD and SAM. The approach involves the use of Pre-trained Language Models (PLMs) such as BERT and ChatGPT and supports different extraction modes and prompt engineering techniques for ChatGPT, as well as different model variants and training strategies for BERT. The proposed approach is integrated with ArDoCo, a tool that detects inconsistencies and recovers trace links between software artifacts. ArDoCo is used for pre-processing the SAD text and parsing the SAM, thus facilitating the traceability link recovery process. In order to assess the performance of the framework, a gold standard of SAD and SAM created from open-source projects is utilized. The evaluation shows that the ChatGPT approach has promising results in relation extraction with a recall of 0.81 and in traceability link recovery with an F1-score of 0.83, while BERT-based models struggle due to the lack of domain-specific training data. |
Freitag, 17. November 2023, 11:30 Uhr
iCal (Download)
Ort: Raum 237 (Gebäude 50.34)
| Vortragende(r) | Gabriel Gehrig |
|---|---|
| Titel | Enabling the Collaborative Collection of Uncertainty Sources Regarding Confidentiality |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Sebastian Hahner |
| Vortragsmodus | in Präsenz |
| Kurzfassung | With digitalization in progress, the amount of sensitive data stored in software systems is increasing. However, the confidentiality of this data can often not be guaranteed, as uncertainties with an impact on confidentiality exist, especially in the early stages of software development. As the consideration of uncertainties regarding confidentiality is still novel, there is a lack of awareness of the topic among software architects. Additionally, the existing knowledge is scattered among researchers and institutions, making it challenging to comprehend and utilize for software architects. Current research on uncertainties regarding confidentiality has focused on analyzing software systems to assess the possibilities of confidentiality violations, as well as the development of methods to classify uncertainties. However, these approaches are limited to the researchers’ observed uncertainties, limiting the generalizability of classification systems, the validity of analysis results, and the development of mitigation strategies. This thesis presents an approach to enable the collection and management of knowledge on uncertainties regarding confidentiality, enabling software architects to comprehend better and identify uncertainties regarding confidentiality. Furthermore, the proposed approach strives to enable collaboration between researchers and practitioners to manage the effort to collect the knowledge and maintain it. To validate this approach, a prototype was developed and evaluated with a user study of 17 participants from software engineering, including 7 students, 5 researchers, and 5 practitioners. Results show that the approach can support software architects in identifying and describing uncertainties regarding confidentiality, even with limited prior knowledge, as they could identify and describe uncertainties correctly in a close-to-real-world scenario in 94.4% of the cases. |
| Vortragende(r) | Niklas Heneka |
|---|---|
| Titel | Software Plagiarism Detection on Intermediate Representation |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Timur Sağlam |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Source code plagiarism is a widespread problem in computer science education. To counteract this, software plagiarism detectors can help identify plagiarized code. Most state-of-the-art plagiarism detectors are token-based. It is common to design and implement a new dedicated language module to support a new programming language. This process can be time-consuming, furthermore, it is unclear whether it is even necessary. In this thesis, we evaluate the necessity of dedicated language modules for Java and C/C++ and derive conclusions for designing new ones. To achieve this, we create a language module for the intermediate representation of LLVM. For the evaluation, we compare it to two existing dedicated language modules in JPlag. While our results show that dedicated language modules are better for plagiarism detection, language modules for intermediate representations show better resilience to obfuscation attacks. |
Freitag, 24. November 2023, 11:30 Uhr
iCal (Download)
Ort: Raum 010 (Gebäude 50.34)
Webkonferenz: https://sdq.kastel.kit.edu/wiki/SDQ-Institutsseminar/Microsoft_Teams
| Vortragende(r) | Nikolai Prjanikov |
|---|---|
| Titel | Conception and Design of Privacy-preserving Software Architecture Templates |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Nicolas Boltz |
| Vortragsmodus | in Präsenz |
| Kurzfassung | The passing of new regulations like the European GDPR has clarified that in the future it will be necessary to build privacy-preserving systems to protect the personal data of its users. This thesis will introduce the concept of privacy templates to help software designers and architects in this matter. Privacy templates are at their core similar to design patterns and provide reusable and general architectural structures which can be used in the design of systems to improve privacy in early stages of design. In this thesis we will conceptualize a small collection of privacy templates to make it easier to design privacy-preserving software systems. Furthermore, the privacy templates will be categorized and evaluated to classify them and assess their quality across different quality dimensions. |
Freitag, 1. Dezember 2023, 11:30 Uhr
iCal (Download)
Ort: Raum 010 (Gebäude 50.34)
Webkonferenz: https://sdq.kastel.kit.edu/wiki/SDQ-Institutsseminar/Microsoft_Teams
| Vortragende(r) | Janne Wagner |
|---|---|
| Titel | Konzept zum Automatisierten Annotieren Rechtlicher Kommentare an einem DSGVO-Modell |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Nicolas Boltz |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Die Einhaltung datenschutzrechtlicher Aspekte sind in der Softwareentwicklung von zunehmender Bedeutung. Um den Prozess der Zusammenarbeit zwischen Softwarearchitekten und Rechtsexperten zu vereinfach und eine selbständigere Arbeitsweise des Softwarearchitekten zu erlangen, wird in dieser Bachelorarbeit ein Konzept zum automatisierten Annotieren rechtlicher Kommentare entwickelt. Im ersten Schritt wird ein Katalog relevanter rechtlicher Kommentare zur DSGVO zusammengestellt, welcher im darauf folgenden Schritt zentraler Bestandteil des Annotationsmechanismus ist. Bei diesem werden die formulierten Kommentare den entsprechenden Modellklassen einer DSGVO-Instanz als Paare zugeordnet und ausgegeben. Durch diese Zuordnung erhält der Softwarearchitekt erste Hinweise auf Datenschutzaspekte, die in seinem Softwaremodell relevant sind und die er im Speziellen berücksichtigen sollte. Darüber hinaus wird er für die DSGVO sensibilisiert und in seiner Modellierung unterstützt. |
| Vortragende(r) | Jean Patrick Mathes |
|---|---|
| Titel | Traceability Link Recovery und Inkonsistenzerkennung zwischen Modellen und informellen Diagrammen mithilfe struktureller Eigenschaften |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Dominik Fuchß |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Diagramme können in der Softwareentwicklung eingesetzt werden, um verschiedene Aspekte des Projekts darzustellen und zu dokumentieren. Die Bachelorarbeit stellt einen Ansatz vor, der für Diagramme in Boxen-und-Linien-Form erkennt, ob darin ein Codemodell oder Architekturmodell abgebildet ist. Dann wird ein Graph-Matching-Algorithmus genutzt, um Nachverfolgbarkeitsverbindungen zwischen Diagramm und Modell zu finden. Sowohl die Texte als auch strukturelle Informationen aus Diagramm und Modell werden dabei genutzt. Die Verbindungen werden verwendet, um Inkonsistenzen zwischen Modell und Diagramm zu finden. Da auch die Struktur berücksichtigt wird, können zum Beispiel Änderungen von Namen als solche erkannt werden. |