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Register a new userFaster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)
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TIm Menzies
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Theorem provers has been used extensively in software engineering for software testing or verification. However, software is now so large and complex that additional architecture is needed to guide theorem provers as they try to generate test suites. The SNAP test suite generator (introduced in this paper) combines the Z3 theorem prover with the following tactic: cluster some candidate tests, then search for valid tests by proposing small mutations to the cluster centroids. This technique effectively removes repeated structures in the tests since many repeated structures can be replaced with one centroid. In practice, SNAP is remarkably effective. For 27 real-world programs with up to half a million variables, SNAP found test suites which were 10 to 750 smaller times than those found by the prior state-of-the-art. Also, SNAP ran orders of magnitude faster and (unlike prior work) generated 100% valid tests.
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Combinatorial testing has been suggested as an effective method of creating test cases at a lower cost. However, industrially applicable tools for modeling and combinatorial test generation are still scarce. As a direct effect, combinatorial testing has only seen a limited uptake in industry that calls into question its practical usefulness. This lack of evidence is especially troublesome if we consider the use of combinatorial test generation for industrial safety-critical control software, such as are found in trains, airplanes, and power plants. To study the industrial application of combinatorial testing, we evaluated ACTS, a popular tool for combinatorial modeling and test generation, in terms of applicability and test efficiency on industrial-sized IEC 61131-3 industrial control software running on Programmable Logic Controllers (PLC). We assessed ACTS in terms of its direct applicability in combinatorial modeling of IEC 61131-3 industrial software and the efficiency of ACTS in terms of generation time and test suite size. We used 17 industrial control programs provided by Bombardier Transportation Sweden AB and used in a train control management system. Our results show that not all combinations of algorithms and interaction strengths could generate a test suite within a realistic cut-off time. The results of the modeling process and the efficiency evaluation of ACTS are useful for practitioners considering to use combinatorial testing for industrial control software as well as for researchers trying to improve the use of such combinatorial testing techniques.
Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics has dominated data analysis in the past; but Bayesian statistics is making a comeback at the forefront of science. In this paper, we give a practical overview of Bayesian statistics and illustrate its main advantages over frequentist statistics for the kinds of analyses that are common in empirical software engineering, where frequentist statistics still is standard. We also apply Bayesian statistics to empirical data from previous research investigating agile vs. structured development processes, the performance of programming languages, and random testing of object-oriented programs. In addition to being case studies demonstrating how Bayesian analysis can be applied in practice, they provide insights beyond the results in the original publications (which used frequentist statistics), thus showing the practical value brought by Bayesian statistics.
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Harsh Beohar
, Mohammad Reza Mousavi (Center for Research on Embeddedn Systems Halmstad University
2014
A major challenge in testing software product lines is efficiency. In particular, testing a product line should take less effort than testing each and every product individually. We address this issue in the context of input-output conformance testing, which is a formal theory of model-based testing. We extend the notion of conformance testing on input-output featured transition systems with the novel concept of spinal test suites. We show how this concept dispenses with retesting the common behavior among different, but similar, products of a software product line.
Context: software projects are common resources in Software Engineering experiments, although these are often selected without following a specific strategy, which reduces the representativeness and replication of the results. An option is the use of preserved collections of software projects, but these must be current, with explicit guidelines that guarantee their updating over a long period of time. Goal: to carry out a systematic secondary study about the strategies to select software projects in empirical studies to discover the guidelines taken into account, the degree of use of project collections, the meta-data extracted and the subsequent statistical analysis conducted. Method: A systematic mapping study to identify studies published from January 2013 to December 2020. Results: 122 studies were identified, of which the 72% used their own guidelines for project selection and the 27% used existent project collections. Likewise, there was no evidence of a standardized framework for the project selection process, nor the application of statistical methods that relates with the sample collection strategy.
Development, deployment and maintenance of networked software has been revolutionized by DevOps practices, which boost system software quality and agile evolution. However, as the Internet of Things (IoT) connects low-power, microcontroller-based devices which take part in larger distributed cyberphysical systems, such low-power IoT devices are not easy to integrate in DevOps workflows. In this paper, we contribute to mitigate this problem by designing Femto-Containers, a new hardware-independent mechanism which enable the virtualization and isolation of software modules embedded on microcontrollers, using an approach extending and adapting Berkeley Packet Filters (eBPF). We implement a Femto-Container hosting engine, which we integrate in a common low-power IoT operating system (RIOT), and is thus enhanced with the ability to start, update or terminate Femto-Containers on demand, securely over a standard IPv6/6LoWPAN network. We evaluate the performance of Femto-Containers in a variety of use cases. We show that Femto-Containers can virtualize and isolate multiple software modules executed concurrently, with very small memory footprint overhead (below 10%) and very small startup time (tens of microseconds) compared to native code execution. We carry out experiments deploying Femto-Containers on a testbed using heterogeneous IoT hardware based on the popular microcontroller architectures Arm Cortex-M, ESP32 and RISC-V. We show that compared to prior work on software-based low-power virtualization and isolation, Femto-Containers offer an attractive trade-off in terms of memory footprint, energy consumption, and security. The characteristics of Femto-Containers satisfy both the requirements of software modules hosting high-level logic coded in a variety of common programming languages, and the constraints of low-level debug snippets inserted on a hot code path.
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