Guillermo Polito

Thomas Dupriez, Guillermo Polito, Steven Costiou et al.

Debugging is one of the most important and time consuming activities in software maintenance, yet mainstream debuggers are not well-adapted to several debugging scenarios. This has led to the research of new techniques covering specific families of complex bugs. Notably, recent research proposes to empower developers with scripting DSLs, plugin-based and moldable debuggers. However, these solutions are tailored to specific use-cases, or too costly for one-time-use scenarios. In this paper we argue that exposing a debugging scripting interface in mainstream debuggers helps in solving many challenging debugging scenarios. For this purpose, we present Sindarin, a scripting API that eases the expression and automation of different strategies developers pursue during their debugging sessions. Sindarin provides a GDB-like API, augmented with AST-bytecode-source code mappings and object-centric capabilities. To demonstrate the versatility of Sindarin, we reproduce several advanced breakpoints and non-trivial debugging mechanisms from the literature.

Matteo Marra, Guillermo Polito, Elisa Gonzalez Boix

Context. Recent studies show that developers spend most of their programming time testing, verifying and debugging software. As applications become more and more complex, developers demand more advanced debugging support to ease the software development process. Inquiry. Since the 70's many debugging solutions were introduced. Amongst them, online debuggers provide a good insight on the conditions that led to a bug, allowing inspection and interaction with the variables of the program. However, most of the online debugging solutions introduce \textit{debugging interference} to the execution of the program, i.e. pauses, latency, and evaluation of code containing side-effects. Approach. This paper investigates a novel debugging technique called \outofplace debugging. The goal is to minimize the debugging interference characteristic of online debugging while allowing online remote capabilities. An \outofplace debugger transfers the program execution and application state from the debugged application to the debugger application, both running in different processes. Knowledge. On the one hand, \outofplace debugging allows developers to debug applications remotely, overcoming the need of physical access to the machine where the debugged application is running. On the other hand, debugging happens locally on the remote machine avoiding latency. That makes it suitable to be deployed on a distributed system and handle the debugging of several processes running in parallel. Grounding. We implemented a concrete out-of-place debugger for the Pharo Smalltalk programming language. We show that our approach is practical by performing several benchmarks, comparing our approach with a classic remote online debugger. We show that our prototype debugger outperforms by a 1000 times a traditional remote debugger in several scenarios. Moreover, we show that the presence of our debugger does not impact the overall performance of an application. Importance. This work combines remote debugging with the debugging experience of a local online debugger. Out-of-place debugging is the first online debugging technique that can minimize debugging interference while debugging a remote application. Yet, it still keeps the benefits of online debugging ( e.g. step-by-step execution). This makes the technique suitable for modern applications which are increasingly parallel, distributed and reactive to streams of data from various sources like sensors, UI, network, etc.

Guillaume Gautier, Guillermo Polito, Rémi Bardenet et al.

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHub and equipped with an extensive documentation.

Martín Dias, Guillermo Polito, Damien Cassou et al.

In software development, version control systems (VCS) provide branching and merging support tools. Such tools are popular among developers to concurrently change a code-base in separate lines and reconcile their changes automatically afterwards. However, two changes that are correct independently can introduce bugs when merged together. We call semantic merge conflicts this kind of bugs. Change impact analysis (CIA) aims at estimating the effects of a change in a codebase. In this paper, we propose to detect semantic merge conflicts using CIA. On a merge, DELTAIMPACTFINDER analyzes and compares the impact of a change in its origin and destination branches. We call the difference between these two impacts the delta-impact. If the delta-impact is empty, then there is no indicator of a semantic merge conflict and the merge can continue automatically. Otherwise, the delta-impact contains what are the sources of possible conflicts.