Formal functional test designs with a test representation language

The application of the category-partition method to the test design phase of hardware, software, or system test development is discussed. The method provides a formal framework for reducing the total number of possible test cases to a minimum logical subset for effective testing. An automatic tool and a formal language were developed to implement the method and produce the specification of test cases

Introducing the Fair and Logical Trade Project

We introduce our framework for logic-based composi- tional e-commerce interaction. We aim to provide open- source software which adds a light-weight formal messag- ing layer to business communications, to increase the ac- cessibility of e-commerce infrastructure to smaller business players. In the process we hope to develop a comprehensive theory of business communication. We present the logical structures and techniques we apply, and provide initial pro- totype testing results

Applying SMT Solvers to the Test Template Framework

The Test Template Framework (TTF) is a model-based testing method for the Z notation. In the TTF, test cases are generated from test specifications, which are predicates written in Z. In turn, the Z notation is based on first-order logic with equality and Zermelo-Fraenkel set theory. In this way, a test case is a witness satisfying a formula in that theory. Satisfiability Modulo Theory (SMT) solvers are software tools that decide the satisfiability of arbitrary formulas in a large number of built-in logical theories and their combination. In this paper, we present the first results of applying two SMT solvers, Yices and CVC3, as the engines to find test cases from TTF's test specifications. In doing so, shallow embeddings of a significant portion of the Z notation into the input languages of Yices and CVC3 are provided, given that they do not directly support Zermelo-Fraenkel set theory as defined in Z. Finally, the results of applying these embeddings to a number of test specifications of eight cases studies are analysed.Comment: In Proceedings MBT 2012, arXiv:1202.582

Test-driven development of embedded control systems: application in an automotive collision prevention system

With test-driven development (TDD) new code is not written until an automated test has failed, and duplications of functions, tests, or simply code fragments are always removed. TDD can lead to a better design and a higher quality of the developed system, but to date it has mainly been applied to the development of traditional software systems such as payroll applications. This thesis describes the novel application of TDD to the development of embedded control systems using an automotive safety system for preventing collisions as an example. The basic prerequisite for test-driven development is the availability of an automated testing framework as tests are executed very often. Such testing frameworks have been developed for nearly all programming languages, but not for the graphical, signal driven language Simulink. Simulink is commonly used in the automotive industry and can be considered as state-of-the-art for the design and development of embedded control systems in the automotive, aerospace and other industries. The thesis therefore introduces a novel automated testing framework for Simulink. This framework forms the basis for the test-driven development process by integrating the analysis, design and testing of embedded control systems into this process. The thesis then shows the application of TDD to a collision prevention system. The system architecture is derived from the requirements of the system and four software components are identified, which represent problems of particular areas for the realisation of control systems, i.e. logical combinations, experimental problems, mathematical algorithms, and control theory. For each of these problems, a concept to systematically derive test cases from the requirements is presented. Moreover two conventional approaches to design the controller are introduced and compared in terms of their stability and performance. The effectiveness of the collision prevention system is assessed in trials on a driving simulator. These trials show that the system leads to a significant reduction of the accident rate for rear-end collisions. In addition, experiments with prototype vehicles on test tracks and field tests are presented to verify the system’s functional requirements within a system testing approach. Finally, the new test-driven development process for embedded control systems is evaluated in comparison to traditional development processes

Automated Testing and Debugging for Big Data Analytics

The prevalence of big data analytics in almost every large-scale software system has generated a substantial push to build data-intensive scalable computing (DISC) frameworks such as Google MapReduce and Apache Spark that can fully harness the power of existing data centers. However, frameworks once used by domain experts are now being leveraged by data scientists, business analysts, and researchers. This shift in user demographics calls for immediate advancements in the development, debugging, and testing practices of big data applications, which are falling behind compared to the DISC framework design and implementation. In practice, big data applications often fail as users are unable to test all behaviors emerging from interleaving dataflow operators, user-defined functions, and framework's code. "Testing based on a random sample" rarely guarantees the reliability and "trial and error" and "print" debugging methods are expensive and time-consuming. Thus, the current practice of developing a big data application must be improved and the tools built to enhance the developer's productivity must adapt to the distinct characteristics of data-intensive scalable computing. By synthesizing ideas from software engineering and database systems, our hypothesis is that we can design effective and scalable testing and debugging algorithms for big data analytics without compromising the performance and efficiency of the underlying DISC framework. To design such techniques, we investigate how we can build interactive and responsive debugging primitives that significantly reduce the debugging time, yet do not pose much performance overhead on big data applications. Furthermore, we investigate how we can leverage data provenance techniques from databases and fault-isolation algorithms from software engineering to pinpoint the minimal subset of failure-inducing inputs efficiently. To improve the reliability of big data analytics, we investigate how we can abstract the semantics of dataflow operators and use them in tandem with the semantics of user-defined functions to generate a minimum set of synthetic test inputs capable of revealing more defects than the entire input dataset.To examine the first hypothesis, we introduce interactive, real-time debugging primitives for big data analytics through innovative and scalable debugging features such as simulated breakpoint, dynamic watchpoint, and crash culprit identification. Second, we design a new automated fault localization approach that combines insights from both the software engineering and database literature to bring delta debugging closer to a reality in the big data applications by leveraging data provenance and by constructing systems optimizations for debugging provenance queries. Lastly, we devise a new symbolic-execution based white-box testing algorithm for big data applications that abstracts the implementation of dataflow operators using logical specifications instead of modeling their implementations and combines them with the semantics of any arbitrary user-defined function. We instantiate the idea of an interactive debugging algorithm as BigDebug, the idea of an automated debugging algorithm as BigSift, and the idea of symbolic execution-based testing as BigTest. Our investigation shows that the interactive debugging primitives can scale to terabytes---our record-level tracing incurs less than 25% overhead on average and provides up to 100% time saving compared to the baseline replay debugger. Second, we observe that by combining data provenance with delta debugging, we can identify the minimum faulty input in just under 30% of the original job execution time. Lastly, we verify that by abstracting dataflow operators using logical specifications, we can efficiently generate the most concise test data suitable for local testing while revealing twice as many faults as prior approaches. Our investigations collectively demonstrate that developer productivity can be significantly improved through effective and scalable testing and debugging techniques for big data analytics, without impacting the DISC framework's performance. This dissertation affirms the feasibility of automated debugging and testing techniques for big data analytics---techniques that were previously considered infeasible for large-scale data processing

GoEliTool for Software Requirements Elicitation using Goal-Oriented Approach

Requirements engineering (RE) is an essential initial stage in software engineering. The RE process begins with the elicitation stage. This stage collects all user requirements that must be fulfilled by the system which will be developed. A goal-oriented approach is an effective approach used to automate the RE process. The development of goal-oriented input document standards is one of the important issues that has not been widely studied. Therefore, this study developed a goal-oriented input document standard for the requirements elicitation process. A tool is developed based on the form of the input document that has been generated. The development of standard forms of input documents begins with literature study and data collection, analysis, design of standard forms of documents, tool design, tool development, and testing. At the analysis stage, a logical framework and element structure is formulated in a goal-oriented approach. Furthermore, the standard form of input documents is developed. The standard form of the document becomes a guideline for developing tools to process data requirements from elicitation results. Tool testing is carried out using black-box testing. The test results show that the tool can work according to the planned function. The trial of the use of the tools was carried out using five requirements datasets. The results of testing and using the tool through the requirements dataset show that GoEliTools can be used to record data on the requirements of several users for the development of an information system.

Enhancing Reuse of Constraint Solutions to Improve Symbolic Execution

Constraint solution reuse is an effective approach to save the time of constraint solving in symbolic execution. Most of the existing reuse approaches are based on syntactic or semantic equivalence of constraints; e.g. the Green framework is able to reuse constraints which have different representations but are semantically equivalent, through canonizing constraints into syntactically equivalent normal forms. However, syntactic/semantic equivalence is not a necessary condition for reuse--some constraints are not syntactically or semantically equivalent, but their solutions still have potential for reuse. Existing approaches are unable to recognize and reuse such constraints. In this paper, we present GreenTrie, an extension to the Green framework, which supports constraint reuse based on the logical implication relations among constraints. GreenTrie provides a component, called L-Trie, which stores constraints and solutions into tries, indexed by an implication partial order graph of constraints. L-Trie is able to carry out logical reduction and logical subset and superset querying for given constraints, to check for reuse of previously solved constraints. We report the results of an experimental assessment of GreenTrie against the original Green framework, which shows that our extension achieves better reuse of constraint solving result and saves significant symbolic execution time.Comment: this paper has been submitted to conference ISSTA 201

DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints