Tezcan Dilshener | The Open University (original) (raw)

Papers by Tezcan Dilshener

For guidance on citations see FAQs.

International Conference on Software Engineering, Jun 2, 2012

The Open University's repository of research publications and other research outputs Improving in... more The Open University's repository of research publications and other research outputs Improving information retrieval-based concept location using contextual relationships Conference or Workshop Item How to cite:

This paper introduces a new model for handling data privacy throughout data lifecycle via the int... more This paper introduces a new model for handling data privacy throughout data lifecycle via the introduction of a policy profile using the Abbreviated Language For Authorization (ALFA) policy language. Our approach extends previous models In three complementary ways: (1) By introducing Administration and Delegation Profile (ADP) in ALFA policy where users and companies can restrict the scope of access/usage policies related to data as well as specify a chain of custody for data (moreover such an approach eases up the tasks of handling users' consent); (2) Thanks to our framework Usage Control System Plus (UCS+) users can monitor the usage of data and revoke its usage upon specific conditions or at will; (3) By introducing new states for policy evaluation, i.e. Admissible/NotAdmissible to filter out those applicable policies that were unauthorized in the first place.

Software developers working on unfamiliar systems are challenged to identify where and how high-l... more Software developers working on unfamiliar systems are challenged to identify where and how high-level concepts are implemented in the source code prior to performing maintenance tasks. Bug localisation is a core program comprehension activity in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source files as the documents to be retrieved, ranked by relevance. Current approaches rely on project history, in particular previously fixed bugs and versions of the source code. Existing IR techniques fall short of providing adequate solutions in finding all the source code files relevant for a bug. Without additional help, bug localisation can become a tedious, timeconsuming and error-prone task. My research contributes a novel algorithm that, given a bug report and the application's source files, uses a combination of lexical and structural information to suggest, in a ranked order, files that may have to be changed to resolve the reported bug without requiring past code and similar reports. I express my thanks and sincere gratitude to my supervisors, Dr. Michel Wermelinger and Dr. Yijun Yu, for believing in my research and in my capabilities. Their dedicated support, direction and the guidance made my journey through the research forest a conquerable experience. Simon Butler for his assistance in using the JIM tool and for the countless proof readings of my work. As my research buddy, he always managed to motivate and encourage me. Prof. Marian Petre for sharing her valuable experience during online seminar sessions and organising the best departmental conferences. Prof. Hongyu Zhang for kindly providing BugLocator and its datasets, Ripon Saha for the BLUiR dataset, Laura Moreno for the LOBSTER dataset and Chu-Pan Wong for giving me access to the source code of his tool BRTracer. Jana B. at our industrial partner, a global financial IT solutions provider located in southern Germany, for providing the artefacts to one of their proprietary application that I used throughout my research and their input on diverse information required. Markus S., Georgi M. and Alan E. for being the proxy at their business locations while I conducted my user study. Also to Markus S. at our industrial partner for his countless hours of discussions on patterns in software applications and source code analysis. My wife Anja for her endless patience, understanding and respect. With her love, encouragement and dedicated coaching, I stood the emotional up and down phases of my research, thus completed my Ph.D. studies. To my two daughters, Denise and Jasmin for allowing their play time with me to be spent on my research instead. Last but not least, thank you to all those who wish to remain unnamed for contributing to my dedication.

Automated software engineering, Oct 10, 2017

Bug localisation is a core program comprehension task in software maintenance: given the observat... more Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history. This is a substantially extended version of our previous 4-page paper (Dilshener et al. 2016). We have added 2 research questions, analysed 2 more projects (Pillar1 and Pillar2), and compared against 3 more approaches (Wong et al. 2014; Youm et al. 2015; Rahman et al. 2015). Sections 4 and 5 are new (except 4.1.3 and 4.1.4) and existing sections have been considerably expanded with new material.

Lecture Notes in Computer Science, 2021

2012 34th International Conference on Software Engineering (ICSE), 2012

PROGRAM COMMITTEE Giuliano Antoniol, ´Ecole Polytechnique de Montréal, Canada Mikio Aoyama, Nanza... more PROGRAM COMMITTEE Giuliano Antoniol, ´Ecole Polytechnique de Montréal, Canada Mikio Aoyama, Nanzan University, Japan Maria Teresa Baldassarre, University of Bari, Italy Ira Baxter, Semantic Designs, USA Xavier Blanc, Université Bordeaux 1, France Mark van den Brand, Technical University Eindhoven, Netherlands ´Arpád Beszédes, University of Szeged, Hungary Danilo Caivano, University of Bari, Italy Gerardo Canfora, University of Sannio, Italy Rafael Capilla, Universidad Rey Juan Carlos, Spain Alexandros ...

2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

Developers working on unfamiliar systems are challenged to accurately identify where and how high... more Developers working on unfamiliar systems are challenged to accurately identify where and how high-level concepts are implemented in the source code. Without additional help, concept location can become a tedious, time-consuming and error-prone task. In this paper we study an industrial financial application for which we had access to the user guide, the source code, and some change requests. We compared the relative importance of the domain concepts, as understood by developers, in the user manual and in the source ...

Automated Software Engineering, 2017