We combine model learning and model checking in a challenging case study involving Linux, Windows and FreeBSD implementations of TCP. We use model learning to infer models of different software components and then apply model checking to fully explore what may happen when these components (e.g. a Linux client and a Windows server) interact. Our analysis reveals several instances in which TCP implementations do not conform to their RFC specifications.

Source code and data relevant for the paper 'Combining Model Learning and Model Checking to Analyze TCP Implementations'

Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present name, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.

/pdf/flexfringe-a-passive-automaton-learning-package-4k3fc4gj0a.pdf

flexfringe: A Passive Automaton Learning Package

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end user devices at the cost of recording and transmitting logs across the Internet to a centralised infrastructure.This paper makes a first step in characterising logging practices with a widely adopted mobile analytics logging library, namely Firebase Analytics. We provide an empirical evaluation of the use of Firebase Analytics in 57 open-source Android applications by studying the evolution of code-bases to understand: a) the needs-in-common that push practitioners to adopt logging practices on mobile devices, and b) the differences in the ways developers use local and remote logging.Our results indicate mobile analytics logs are less pervasive and less maintained than traditional logging code. Based on our analysis, we believe logging using mobile analytics is more user centered compared to traditional logging, where the latter is mainly used to record information for debugging purposes.

/pdf/logging-practices-with-mobile-analytics-an-empirical-study-ikc3amqdlu.pdf

Logging Practices with Mobile Analytics: An Empirical Study on Firebase

Contemporary software development strongly relies on software monitoring for different purposes, such as detecting abnormal behaviour or finding performance issues. These analyses are enabled by means of log data. The richness of log information has drawn the attention of researchers, who have put significant effort in software monitoring and log analysis techniques. Such knowledge, however, is currently spread. Moreover, we have no conceptual framework to explain the research field. In this paper, we perform a systematic literature review on logging techniques for software monitoring. More specifically, we explore the existing contemporary research on log engineering, infrastructure, and analysis. To that aim, we study 96 papers that appeared on top-level peer-reviewed conferences and journals. We then propose the Contemporary Logging Framework, a conceptual framework that maps the entire research field in four dimensions and 13 ramifications (i.e., research focuses). Finally, based on all the knowledge we gained, we propose a list of next steps that will help researchers in moving the field forward.

Contemporary Software Monitoring: A Systematic Literature Review.

Engineering modern large-scale software requires software developers to not solely focus on writing code, but also to continuously examine monitoring data to reason about the dynamic behavior of their systems. These additional monitoring responsibilities for developers have only emerged recently, in the light of DevOps culture. Interestingly, software development activities happen mainly in the IDE, while reasoning about production monitoring happens in separate monitoring tools. We propose an approach that integrates monitoring signals into the development environment and workflow. We conjecture that an IDE with such capability improves the performance of developers as time spent continuously context switching from development to monitoring would be eliminated. This paper takes a first step towards understanding the benefits of a possible monitoring-aware IDE. We implemented a prototype of a Monitoring-Aware IDE, connected to the monitoring systems of Adyen, a large-scale payment company that performs intense monitoring in their software systems. Given our results, we firmly believe that monitoring-aware IDEs can play an essential role in improving how developers perform monitoring.

/pdf/monitoring-aware-ides-4fkxc2k5bn.pdf

Monitoring-aware IDEs

Passive learning techniques infer graph models on the behavior of a system from large trace logs. The research community has been dedicating great effort in making passive learning techniques more scalable and ready to use by industry. However, there is still a lack of empirical knowledge on the usefulness and applicability of such techniques in large scale real systems. To that aim, we conducted action research over nine months in a large payment company. Throughout this period, we iteratively applied passive learning techniques with the goal of revealing useful information to the development team. In each iteration, we discussed the findings and challenges to the expert developer of the company, and we improved our tools accordingly. In this paper, we present evidence that passive learning can indeed support development teams, a set of lessons we learned during our experience, a proposed guide to facilitate its adoption, and current research challenges.

/pdf/an-experience-report-on-applying-passive-learning-in-a-large-1qlf1w1r4y.pdf

An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

Analyzing large numbers of log entries can be challenging, especially when there are many short log entries that each describe only one execution of the system, either successful or unsuccessful. How can one determine whether the system is working correctly, based on these logs? The logs that are of interest (e.g., log entries pointing towards some anomaly in the system) may be hidden between all the logs that are of less interest. Luckily, there are so-called passive learning tools that infer a (graph) model from such set of logs, which allows the user to oversee all paths that were taken in the system. In this thesis, we discuss the opportunities for passive learning for Adyen, a large-scale payment company. We compare three different open source passive learning tools (namely Synoptic, InvariMint, and DFASAT) in terms of runtime performance and output complexity, and show that all tools struggle with an increasing input size. We also share the results of a survey we conducted under developers to identify their perceptions, and for which purpose(s) they would use such tools. Furthermore, we provide six examples of different types of analyses that are possible with passive learning (such as finding bugs, comparing within a context, and analyzing timings), and that are useful for the company. We include a short guide on how to adopt passive learning, and what we had to change in one of the tools to make it so useful. Finally, we show how a graph difference tool can help to compare different graphs, for example over different time intervals. This tool highlights differences in both structure and frequencies. Altogether, this shows what passive learning brings to Adyen.

/pdf/what-does-passive-learning-bring-to-adyen-34ub0x6p5u.pdf

What Does Passive Learning Bring To Adyen

A lot of interesting information can be extracted from the way users interact with a website. The regular way of extracting this information is through Google Analytics. Unfortunately, Google Analytics has two limitations. First, because Google Analytics uses a pageview as unit of measurement, it is unable to describe all user interactions. For example, tracking users on different devices is hard. Second, the reports generated by Google Analytics are not tailored for a specific business. For example, Google Analytics’ support for key performance indicators is very limited. We investigated how to build a system that is capable of providing these deeper insights. An event is used as the unit of measurement to provide the flexibility that is required to track all user interactions. We designed the system to cope with the data volume required by Magnet.me. This is done by using MongoDB as storage engine. We have shown that our system satisfies our requirements by performing performance tests.

/pdf/tracking-events-at-magnet-me-38l12mwfiz.pdf

Tracking Events at Magnet.me

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An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

What Does Passive Learning Bring To Adyen

Tracking Events at Magnet.me