Demo Data from VLDB 2026 / reviewers in the wild / expert
Saahil Ognawala
dblp:150/8866
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
2 papers |
Program analysis · 82% Empirical software engineering · 18% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 9 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › memory safety › memory error detection
buffer overflow detection |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Systems and software security
vulnerability discovery |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Program analysis
dynamic analysis |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Program analysis › symbolic execution
dynamic symbolic execution |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Program analysis › static analysis
interprocedural analysis |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Program analysis
static analysis |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Program analysis
symbolic execution |
0.2 | 1 | 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic execution · ASE 2016 |
Empirical software engineering › software engineering research methodology
empirical software engineering methods |
0.2 | 1 | 2015 | Fast Feedback Cycles in Empirical Software Engineering Research · ICSE (2) 2015 |
Empirical software engineering
mining software repositories |
0.1 | 1 | 2015 | Fast Feedback Cycles in Empirical Software Engineering Research · ICSE (2) 2015 |
Methods — techniques the papers use, named apart from their topics
static code analysis · 0.5constraint solving · 0.5proof-of-concept · 0.2domain knowledge integration · 0.2data mining · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | MACKE: compositional analysis of low-level vulnerabilities with symbolic executionabstractConcolic (concrete+symbolic) execution has recently gained popularity as an effective means to uncover non-trivial vulnerabilities in software, such as subtle buffer overflows. However, symbolic execution tools that are designed to optimize statement coverage often fail to cover potentially vulnerable code because of complex system interactions and scalability issues of constraint solvers. In this paper, we present a tool (MACKE) that is based on the modular interactions inferred by static code analysis, which is combined with symbolic execution and directed inter-procedural path exploration. This provides an advantage in terms of statement coverage and ability to uncover more vulnerabilities. Our tool includes a novel feature in the form of interactive vulnerability report generation that helps developers prioritize bug fixing based on severity scores. A demo of our tool is available at https://youtu.be/icC3jc3mHEU. Saahil Ognawala, Martín Ochoa, Alexander Pretschner, Tobias Limmer |
ASE | 1 |
| 2015 | Fast Feedback Cycles in Empirical Software Engineering ResearchabstractBackground/Context: Gathering empirical knowledge is a time consuming task and the results from empirical studies often are soon outdated by new technological solutions. As a result, the impact of empirical results on software engineering practice is often not guaranteed.Objective/Aim: In this paper, we summarise the ongoing discussion on "Empirical Software Engineering 2.0" as a way to improve the impact of empirical results on industrial practices. We propose a way to combine data mining and analysis with domain knowledge to enable fast feedback cycles in empirical software engineering research.Method: We identify the key concepts on gathering fast feedback in empirical software engineering by following an experience-based line of reasoning by argument. Based on the identified key concepts, we design and execute a small proof of concept with a company to demonstrate potential benefits of the approach.Results: In our example, we observed that a simple double feedback mechanism notably increased the precision of the data analysis and improved the quality of the knowledge gathered.Conclusion: Our results serve as a basis to foster discussion and collaboration within the research community for a development of the idea. Antonio Vetrò, Saahil Ognawala, Daniel Méndez 0001, Stefan Wagner 0001 |
ICSE (2) | 2 |
| 2014 | Where do we stand in requirements engineering improvement today?: first results from a mapping studyabstractContext: Requirements engineering process improvement (REPI) approaches have gained much attention in research and practice. Goal: So far, there is no comprehensive view on the research in REPI in terms of solutions and current state of reported evidence. We aims to provide an overview on the existing solutions, their underlying principles and their research type facets, i.e. their state of empirical evidence. Method: To this end, we conducted a systematic mapping study of the REPI publication space. Results: This paper reports on the first findings regarding research type facets of the contributions as well as selected methodological principles. We found a strong focus in the existing research on solution proposals for REPI approaches that concentrate on normative assessments and benchmarks of the RE activities rather than on holistic RE improvements according to individual goals of companies. Conclusions: We conclude, so far, that there is a need to broaden the work and to investigate more problem-driven REPI which also targets the improvement of the quality of the underlying RE artefacts, which currently seem out of scope. Daniel Méndez 0001, Saahil Ognawala, Stefan Wagner 0001, Maya Daneva |
ESEM | 2 |