Family-Based Model Checking with mCRL2

\u3cp\u3eFamily-based model checking targets the simultaneous verfication of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines (SPLs). We present an approach for family-based verification based on the feature μ-calculus μL\u3csub\u3ef\u3c/sub\u3e, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants (a family of products). Via an embedding into the modal μ-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for μLf becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\u3c/p\u3

Experiences with formal engineering: model-based specification, implementation and testing of a software bus at Neopost

We report on the actual industrial use of formal methods during the development of a software bus. During an internship at Neopost Inc., of 14 weeks, we developed the server component of a software bus, called the XBus, using formal methods during the design, validation and testing phase: we modeled our design of the XBus in the process algebra mCRL2, validated the design using the mCRL2-simulator, and fully automatically tested our implementation with the model-based test tool JTorX. This resulted in a well- tested software bus with a maintainable architecture. Writing the model (mdev), simulating it, and testing the implementation with JTorX only took 17% of the total development time. Moreover, the errors found with model-based testing would have been hard to find with conventional test methods. Thus, we show that formal engineering can be feasible, beneficial and cost-effective.\ud The findings above, reported earlier by us in (Sijtema et al., 2011) [1], were well- received, also in industrially oriented conferences (Ferreira and Romanenko, 2010) [2] and [3]. In this paper, we look back on the case study, and carefully analyze its merits and shortcomings. We reflect on (1) the added benefits of model checking, (2) model completeness and (3) the quality and performance of the test process.\ud Thus, in a second phase, after the internship, we model checked the XBus protocol—this was not done in [1] since the Neopost business process required a working implementation after 14 weeks. We used the CADP tool evaluator4 to check the behavioral requirements obtained during the development. Model checking did not uncover errors in model mdev, but revealed that model mdev was neither complete nor optimized: in particular, requirements to the so-called bad weather behavior (exceptions, unexpected inputs, etc.) were missing. Therefore, we created several improved models, checked that we could validate them, and used them to analyze quality and performance of the test process. Model checking was expensive: it took us approx. 4 weeks in total, compared to 3 weeks for the entire model-based testing approach during the internship.\ud In the second phase, we analyzed the quality and performance of the test process, where we looked at both code and model coverage. We found that high code coverage (almost 100%) is in most cases obtained within 1000 test steps and 2 minutes, which matches the fact that the faults in the XBus were discovered within a few minutes.\ud Summarizing, we firmly believe that the formal engineering approach is cost-effective, and produces high quality software products. Model checking does yield significantly better models, but is also costly. Thus, system developers should trade off higher model quality against higher costs

Tools and Algorithms for the Construction and Analysis of Systems

This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems

Modelling and verifying IEEE Std 11073-20601 session setup using mCRL2

In this paper we advocate that formal verification should be a part of the development of a communication standard; in a short period of time issues are uncovered that have been in the standard for a number of years, and all subtleties in the correctness of the protocol are understood. We model and verify the session setup protocol that is part of the IEEE 11073-20601:2008 standard for communication between personal health devices. We identify a number of issues present in the standards document. Discussion with a member of the standards committee unveiled that most, but not all, of the identified issues are fixed in the IEEE 11073-20601:2010 version of the standard. In addition, the correctness of the protocol, including the fixes, is assessed. For this, properties of the session setup protocol are formulated, and using the model checker mCRL2 it is verified whether the model satisfies these properties. We show that the session setup protocol is flawed, and propose a straightforward way to fix this issue

Feature Nets: behavioural modelling of software product lines

Software product lines (SPL) are diverse systems that are developed using a dual engineering process: (a)family engineering defines the commonality and variability among all members of the SPL, and (b) application engineering derives specific products based on the common foundation combined with a variable selection of features. The number of derivable products in an SPL can thus be exponential in the number of features. This inherent complexity poses two main challenges when it comes to modelling: Firstly, the formalism used for modelling SPLs needs to be modular and scalable. Secondly, it should ensure that all products behave correctly by providing the ability to analyse and verify complex models efficiently. In this paper we propose to integrate an established modelling formalism (Petri nets) with the domain of software product line engineering. To this end we extend Petri nets to Feature Nets. While Petri nets provide a framework for formally modelling and verifying single software systems, Feature Nets offer the same sort of benefits for software product lines. We show how SPLs can be modelled in an incremental, modular fashion using Feature Nets, provide a Feature Nets variant that supports modelling dynamic SPLs, and propose an analysis method for SPL modelled as Feature Nets. By facilitating the construction of a single model that includes the various behaviours exhibited by the products in an SPL, we make a significant step towards efficient and practical quality assurance methods for software product lines