Grammar-based fuzzing using input features

In grammar-based fuzz testing, a formal grammar is used to produce test inputs that are syntactically valid in order to reach the business logic of a program under test. In this setting, it is advantageous to ensure a high diversity of inputs to test more of the program's behavior. How can we characterize features that make inputs diverse and associate them with the execution of particular parts of the program? Previous work does not answer this question to satisfaction, with most attempts mainly considering superficial features defined by the structure of the grammar such as the presence of production rules or terminal symbols, regardless of their context. We present a measure of input coverage called k-path coverage, which takes into account combinations of grammar entities up to a given context depth k, and makes it possible to efficiently express, assess, and achieve input diversity. In a series of experiments, we demonstrate and evaluate how to systematically attain k-path coverage, how it correlates with code coverage and can thus be used as its predictor. By automatically inferring explicit associations between k-path features and the coverage of individual methods we further show how to generate inputs that specifically target the execution of given code locations. We expect the presented instrument of k-paths to prove useful in numerous additional applications such as assessing the quality of grammars, serving as an adequacy criterion for input test suites, enabling test case prioritization, facilitating program comprehension, and perhaps beyond.Im Bereich des grammatik-basierten Fuzz-Testens benutzt man eine formale Grammatik, um Testeingaben zu produzieren, welche syntaktisch korrekt sind, mit dem Ziel die Geschäftslogik eines zu testenden Programms zu erreichen. Dafür ist es vorteilhaft eine hohe Diversität der Eingaben zu sichern, um mehr vom Verhalten des Programms testen zu können. Wie kann man Merkmale charakterisieren, die Eingaben vielfältig machen und diese mit der Ausführung bestimmter Programmteile in Verbindung bringen? Bisherige Ansätze liefern darauf keine ausreichende Antwort, denn meistens betrachten sie oberflächliche, durch die Grammatikstruktur definierte Merkmale, wie das Vorhandensein von Produktionsregeln oder Terminalen, unabhängig von ihrem Verwendungskontext. Wir präsentieren ein Maß für Eingabeabdeckung, genannt -path Abdeckung, welche Kombinationen von Grammatikelementen bis zu einer vorgegebenen Kontexttiefe berücksichtigt und es ermöglicht, die Diversität von Eingaben effizient auszudrücken, zu bewerten und zu erzielen. Mit Experimenten zeigen und evaluieren wir, wie man gezielt -path Abdeckung erreicht und wie sie mit der Codeabdeckung zusammenhängt und diese somit vorhersagen kann. Ferner zeigen wir wie automatisches Erlernen expliziter Assoziationen zwischen Merkmalen und der Abdeckung einzelner Methoden die Erzeugung von Eingaben ermöglicht, welche auf die Ausführung bestimmter Codestellen abzielen. Wir rechnen damit, dass sich -paths als ein vielseitiges Instrument beweisen, dessen Anwendung über solche Gebiete, wie z.B. Messung der Qualität von Grammatiken und Eingabe-Testsuiten, Testfallpriorisierung, oder Erleichterung von Programmverständnis, hinausgeht

A new generation of user-friendly and machine learning-accelerated methods for protein pKa calculations

The ability to sense and react to external and internal pH changes is a survival requirement for any cell. pH homeostasis is tightly regulated, and even minor disruptions can severely impact cell metabolism, function, and survival. The pH dependence of proteins can be attributed to only 7 out of the 20 canonical amino acids, the titratable amino acids that can exchange protons with water in the usual 0-14 pH range. These amino acids make up for approximately 31% of all amino acids in the human proteome, meaning that, on average, roughly one-third of each protein is sensitive not only to the medium pH but also to alterations in the electrostatics of its surroundings. Unsurprisingly, protonation switches have been associated with a wide array of protein behaviors, including modulating the binding affinity in protein-protein, protein-ligand, or protein-lipid systems, modifying enzymatic activity and function, and even altering their stability and subcellular location. Despite its importance, our molecular understanding of pHdependent effects in proteins and other biomolecules is still very limited, particularly in big macromolecular complexes such as protein-protein or membrane protein systems. Over the years, several classes of methods have been developed to provide molecular insights into the protonation preference and dependence of biomolecules. Empirical methods offer cheap and competitive predictions for time- or resource-constrained situations. Albeit more computationally expensive, continuum electrostatics-based are a cost-effective solution for estimating microscopic equilibrium constants, pKhalf and macroscopic pKa. To study pH-dependent conformational transitions, constant-pH molecular dynamics (CpHMD) is the appropriate methodology. Unfortunately, given the computational cost and, in many cases, the difficulty associated with using CE-based and CpHMD, most researchers overuse empirical methods or neglect the effect of pH in their studies. Here, we address these issues by proposing multiple pKa predictor methods and tools with different levels of theory designed to be faster and accessible to more users. First, we introduced PypKa, a flexible tool to predict Poisson–Boltzmann/Monte Carlo-based (PB/MC) pKa values of titratable sites in proteins. It was validated with a large set of experimental values exhibiting a competitive performance. PypKa supports CPU parallel computing and can be used directly on proteins obtained from the Protein Data Bank (PDB) repository or molecular dynamics (MD) simulations. A simple, reusable, and extensible Python API is provided, allowing pKa calculations to be easily added to existing protocols with a few extra lines of code. This capability was exploited in the development of PypKa-MD, an easy-to-use implementation of the stochastic titration CpHMD method. PypKa-MD supports GROMOS and CHARMM force fields, as well as modern versions of GROMACS. Using PypKa’s API and consequent abstraction of PB/MC contributed to its greatly simplified modular architecture that will serve as the foundation for future developments. The new implementation was validated on alanine-based tetrapeptides with closely interacting titratable residues and four commonly used benchmark proteins, displaying highly similar and correlated pKa predictions compared to a previously validated implementation. Like most structural-based computational studies, the majority of pKa calculations are performed on experimental structures deposited in the PDB. Furthermore, there is an ever-growing imbalance between scarce experimental pKa values and the increasingly higher number of resolved structures. To save countless hours and resources that would be spent on repeated calculations, we have released pKPDB, a database of over 12M theoretical pKa values obtained by running PypKa over 120k protein structures from the PDB. The precomputed pKa estimations can be retrieved instantaneously via our web application, the PypKa Server. In case the protein of interest is not in the pKPDB, the user may easily run PypKa in the cloud either by uploading a custom structure or submitting an identifier code from the PBD or UniProtKB. It is also possible to use the server to get structures with representative pH-dependent protonation states to be used in other computational methods such as molecular dynamics. The advent of artificial intelligence in biological sciences presented an opportunity to drastically accelerate pKa predictors using our previously generated database of pKa values. With pKAI, we introduced the first deep learning-based predictor of pKa shifts in proteins trained on continuum electrostatics data. By combining a reasonable understanding of the underlying physics, an accuracy comparable to that of physics-based methods, and inference time speedups of more than 1000 ×, pKAI provided a game-changing solution for fast estimations of macroscopic pKa from ensembles of microscopic values. However, several limitations needed to be addressed before its integration within the CpHMD framework as a replacement for PypKa. Hence, we proposed a new graph neural network for protein pKa predictions suitable for CpHMD, pKAI-MD. This model estimates pH-independent energies to be used in a Monte Carlo routine to sample representative microscopic protonation states. While developing the new model, we explored different graph representations of proteins using multiple electrostatics-driven properties. While there are certainly many new features to be introduced and a multitude of development to be expanded, the selection of methods and tools presented in this work poses a significant improvement over the alternatives and effectively constitutes a new generation of user-friendly and machine learning-accelerated methods for pKa calculations

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation