Write a Blog >>
ICSE 2020
Wed 24 June - Thu 16 July 2020
Wed 8 Jul 2020 16:44 - 16:50 at Goguryeo - A11-Performance and Analysis Chair(s): Pooyan Jamshidi

Programs tend to provide a broad range of features, and different typologies of users tend to use only a subset of these features. For this reason, and because unnecessary functionality can be harmful in terms of both performance and security, recently we have witnessed an increasing interest in debloating techniques—techniques for reducing the size of a program by eliminating (possibly) unneeded features. Most existing debloating techniques tend to focus on program-size reduction alone, by producing a reduced program that behaves correctly for a provided set of inputs. Although effective with respect to their stated goal, these approaches ignore other important aspects of debloating and ultimately solve a simplified formulation of the problem. We believe that program debloating is a multifaceted issue, in which different, possibly conflicting goals must be considered and suitably accounted for. In this spirit, we propose a general approach that allows for formulating program debloating as a multi-objective optimization problem. Given a program to be debloated, our approach lets users specify (1) a usage profile for the program (i.e., a set of inputs with associated usage probabilities), (2) the factors of interest for the debloating task at hand, and (3) the relative importance of these factors. Based on this information, the approach defines a suitable objective function, so as to be able to associate a score to every possible reduced program, and tries to generate an optimal solution, that is, one that maximizes the objective function. To provide concrete evidence of the usefulness of our approach, we also present and evaluate Debop, a specific instance of the approach that considers three objectives: size reduction, attack surface reduction, and generality (i.e., extent to which the reduced program behaves correctly for the inputs in p’s usage profile). Our results, albeit still preliminary, are promising, in that they show that our approach can be effective in generating debloated programs that achieve good trade-offs between the different factors involved in the debloating process. Our results also provide insights on the performance of our general approach when compared to a specialized single-goal technique.

Wed 8 Jul

Displayed time zone: (UTC) Coordinated Universal Time change

16:05 - 17:05
A11-Performance and AnalysisNew Ideas and Emerging Results / Journal First / Technical Papers / Demonstrations at Goguryeo
Chair(s): Pooyan Jamshidi University of South Carolina
16:05
3m
Talk
Nimbus: Improving the Developer Experience for Serverless ApplicationsDemo
Demonstrations
Robert Chatley Imperial College London, Thomas Allerton Starling Bank
Pre-print
16:08
8m
Talk
Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case GenerationJ1
Journal First
Giovanni Grano University of Zurich, Christoph Laaber University of Zurich, Annibale Panichella Delft University of Technology, Sebastiano Panichella Zurich University of Applied Sciences
Link to publication DOI Pre-print
16:16
8m
Talk
What's Wrong with My Benchmark Results? Studying Bad Practices in JMH BenchmarksJ1
Journal First
Diego Costa Concordia University, Canada, Cor-Paul Bezemer University of Alberta, Canada, Philipp Leitner Chalmers University of Technology & University of Gothenburg, Artur Andrzejak Heidelberg University
16:24
12m
Talk
Towards the Use of the Readily Available Tests from the Release Pipeline as Performance Tests. Are We There Yet?ACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Zishuo Ding University of Waterloo, Canada, Jinfu Chen Concordia University, Canada, Weiyi Shang Concordia University
Pre-print
16:36
8m
Talk
ModGuard: Identifying Integrity & Confidentiality Violations in Java ModulesJ1
Journal First
Andreas Dann Paderborn University, Ben Hermann Paderborn University, Eric Bodden Heinz Nixdorf Institut, Paderborn University and Fraunhofer IEM
Link to publication DOI
16:44
6m
Talk
Program Debloating via Stochastic OptimizationNIER
New Ideas and Emerging Results
Qi Xin Georgia Institute of Technology, Myeongsoo Kim Georgia Institute of Technology, Qirun Zhang Georgia Institute of Technology, USA, Alessandro Orso Georgia Tech
16:50
8m
Talk
The ORIS Tool: Quantitative Evaluation of Non-Markovian SystemsJ1
Journal First
Marco Paolieri University of Southern California, Marco Biagi University of Florence, Laura Carnevali University of Florence, Enrico Vicario University of Florence