My main research interests are in programming languages, software systems and program analysis. During my PhD, I focused my work on the study of performance scalability of software systems. In the last two years, I have investigated how program analysis techniques, such as symbolic execution, can be exploited in the context of cybersecurity.
coppa [at] dis.uniroma1.it
DIAG, Via Ariosto 25, Rome - 1st floor, room B118
aprof - input-sensitive profilingaprof is a Valgrind tool for performance profiling designed to help developers discover hidden asymptotic inefficiencies in the code. From one or more runs of a program, aprof measures how the performance of individual routines scales as a function of the input size, yielding clues to its growth rate.
Related papers: [CDF-PLDI12] [CDFM-CGO14] [CDF-TSE14] [C-VAL14]
Hadoop Internals - DiagramsThis project contains several diagrams describing Apache Hadoop internals (2.3.0 or later).
NearestFit - predicting MapReduce performanceThe NearestFit progress indicator targets accuracy of progress predictions for MapReduce jobs in the presence of data skewness and super-linear computations. This is achieved combining performance profiling, machine learning techniques, and data streaming algorithms.
Related papers: [CF-SOCC15]
- [CDD-ASE17] Emilio Coppa, Daniele Cono D’Elia, Camil Demetrescu. Rethinking Pointer Reasoning in Symbolic Execution. Accepted at the 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017). [PDF]
- [BCDD-CSCML17] Roberto Baldoni, Emilio Coppa, Daniele Cono D’Elia, Camil Demetrescu. Assisting Malware Analysis with Symbolic Execution: a Case Study. 2017 International Symposium on Cyber Security Cryptography and Machine Learning (CSCML 2017). [DOI] [PDF]
- [BCDDF-TR16] Roberto Baldoni, Emilio Coppa, Daniele Cono D'Elia, Camil Demetrescu, Irene Finocchi. A Survey of Symbolic Execution Techniques. Technical report, 2016. [ArXiv]
- [CF-SOCC15] Emilio Coppa and Irene Finocchi. On data skewness, stragglers, and MapReduce progress indicators. Sixth ACM Symposium on Cloud Computing (SoCC'15), pp 139-152, 2015. [DOI] [PDF] [SLIDES]
- [C-VAL14] Emilio Coppa. An interactive visualization framework for performance analysis. 8th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2014), pp 159-164, 2014. [DOI] [PDF] [SLIDES] [PROJECT SITE]
- [CDF-TSE14] Emilio Coppa, Camil Demetrescu, and Irene Finocchi. Input-Sensitive Profiling. IEEE Transactions on Software Engineering (IEEE TSE'14), 40(12), pp 1185-1205, 2014.[DOI] [PDF] [PROJECT SITE]
- [CDFM-CGO14] Emilio Coppa, Camil Demetrescu, Irene Finocchi, and Romolo Marotta. Estimating the Empirical Cost Function of Routines with Dynamic Workloads. 12th IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2014), pp 230-239, 2014. [DOI] [PDF] [SLIDES] [PROJECT SITE]
- [CDF-PLDI12] Emilio Coppa, Camil Demetrescu, and Irene Finocchi. Input-Sensitive Profiling. 33rd ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI 2012), pp 89-98, 2012. [DOI] [PDF] [SLIDES] [PROJECT SITE]