DoubleTake

Achieving comprehensive code quality and ensuring software reliability are critical goals in modern software engineering. This paper delves into advanced strategies that encompass both technical and organizational practices, aiming to enhance code quality and boost software reliability. Key strategies include the implementation of rigorous code review processes, adoption of automated testing frameworks, and the utilization of static and dynamic code analysis tools. Firstly, rigorous code review processes are fundamental for maintaining high standards of code quality. By fostering a collaborative environment where peer reviews are routine, teams can catch potential issues early, ensure adherence to coding standards, and promote knowledge sharing. This practice not only improves code quality but also enhances team cohesion and expertise. Secondly, automated testing frameworks are indispensable in achieving comprehensive test coverage and reducing the incidence of bugs. Unit tests, integration tests, and end-to-end tests, when automated, provide continuous feedback and ensure that code changes do not introduce new defects. This continuous testing approach is crucial for maintaining software reliability, especially in agile and DevOps environments where rapid iterations are common. Furthermore, the integration of static and dynamic code analysis tools into the development pipeline offers additional layers of quality assurance. Static analysis tools inspect the codebase for potential vulnerabilities, code smells, and compliance with coding standards without executing the program. Dynamic analysis tools, on the other hand, evaluate the software during runtime to detect memory leaks, concurrency issues, and performance bottlenecks. Together, these tools provide a comprehensive assessment of the code’s health and reliability. Moreover, fostering a culture of continuous improvement and learning within the development team is essential. Regular training sessions, workshops, and knowledge-sharing activities help in keeping the team updated with the latest advancements in software engineering practices and tools. Encouraging a mindset of quality-first and reliability among developers ensures long-term benefits and sustainability. In conclusion, achieving comprehensive code quality and ensuring software reliability require a multifaceted approach that integrates rigorous code reviews, automated testing frameworks, and both static and dynamic analysis tools. Coupled with a strong culture of continuous learning and improvement, these strategies collectively contribute to the development of robust, reliable, and high-quality software systems. Keywords: Software Reliability, Code Quality, Advanced, Strategies, Comprehensive.

Static and dynamic analysis techniques for bug detection have significantly improved in the last decades, and are today implemented in industry-strength tools and routinely applied by developers. Nevertheless, it is still difficult to deal with bugs located in OTS software, since developers lack the source code and/or knowledge about their internals to fix these bugs. In this paper, we propose an approach for fixing memory leak bugs in OTS software, that leverages dynamic binary analysis tools to find bugs, and binary code rewriting to patch them. Patching will allow to rejuvenate OTS-based software less frequently, thus further improving the availability of applications using this approach. Future work will implement this approach in a prototype, and validate it on real memory leaks found in complex software.

Context: In C, low-level errors, such as buffer overflow and use-after-free, are a major problem, as they cause security vulnerabilities and hard-to-find bugs. C lacks automatic checks, and programmers cannot apply defensive programming techniques because objects (e.g., arrays or structs) lack run-time information about bounds, lifetime, and types. Inquiry: Current approaches to tackling low-level errors include dynamic tools, such as bounds or type checkers, that check for certain actions during program execution. If they detect an error, they typically abort execution. Although they track run-time information as part of their runtimes, they do not expose this information to programmers. Approach: We devised an introspection interface that allows C programmers to access run-time information and to query object bounds, object lifetimes, object types, and information about variadic arguments. This enables library writers to check for invalid input or program states and thus, for example, to implement custom error handling that maintains system availability and does not terminate on benign errors. As we assume that introspection is used together with a dynamic tool that implements automatic checks, errors that are not handled in the application logic continue to cause the dynamic tool to abort execution. Knowledge: Using the introspection interface, we implemented a more robust, source-compatible version of the C standard library that validates parameters to its functions. The library functions react to otherwise undefined behavior; for example, they can detect lurking flaws, handle unterminated strings, check format string arguments, and set errno when they detect benign usage errors. Grounding: Existing dynamic tools maintain run-time information that can be used to implement the introspection interface, and we demonstrate its implementation in Safe Sulong, an interpreter and dynamic bug-finding tool for C that runs on a Java Virtual Machine and can thus easily expose relevant run-time information. Importance: Using introspection in user code is a novel approach to tackling the long-standing problem of low-level errors in C. As new approaches are lowering the performance overhead of run-time information maintenance, the usage of dynamic runtimes for C could become more common, which could ultimately facilitate a more widespread implementation of such an introspection interface.

Dynamic program analysis encompasses the development of techniques and tools for analyzing computer software by exploiting information gathered from a program at runtime. The impressive amounts of data collected by dynamic analysis tools require efficient indexing and compression schemes, as well as on-line algorithmic techniques for mining relevant information on-the-fly in order to identify frequent events, hidden software patterns, or undesirable behaviors corresponding to bugs, malware, or intrusions. The paper explores how recent results in algorithmic theory for data-intensive scenarios can be applied to the design and implementation of dynamic program analysis tools, focusing on two important techniques: sampling and streaming.

Wasmati: An efficient static vulnerability scanner for WebAssembly

The Department of Homeland Security (DHS) considers the effects of hypothetical terrorism scenarios distinguished by many dimensions including: perpetrator; target; location; weapon; and delivery method. For each scenario, DHS requires a computationally rapid, in-house (secure) tool for translating impact effects or “driving variables” (e.g. capital destruction, clean-up expenditures, etc.) into economic implication variables (e.g. GDP in the short and long run, regional output in the short and long run, and economic welfare). We use a detailed, dynamic, multi-regional CGE model to generate elasticities E(s,d,v) of 9 implication variables (v) with respect to 14 driving variables (s) occurring as a result of incidents in any of the US’s 436 congressional districts (d). Equipped with these elasticities, DHS can apply trivial calculations to estimate the national and regional economic implications of an enormous variety of scenarios. Rose et al. (Economic consequence analysis tool (E-CAT), Springer, Tokyo, 2017) also propose a CGE-based rapid calculation tool for translating terrorism-related driving variables into economic implication variables. They refer to this tool as E-CAT (Economic Consequence Analysis Tool). Compared with E-CAT, our tool has a more general coverage of economic variables (both driving and implication variables) and introduces regional and dynamic dimensions. In view of the similarities and differences between our approach and E-CAT, we title the tool created here as GRAD-E-CAT (Generalized, Regional And Dynamic Economic Consequence Analysis Tool).

Dynamic analysis tools, such as those that detect data-races, verify memory safety, and identify information flow, have become a vital part of testing and debugging complex software systems. While these tools are powerful, their slow speed often limits how effectively they can be deployed in practice. Hybrid analysis speeds up these tools by using static analysis to decrease the work performed during dynamic analysis. In this paper we argue that current hybrid analysis is needlessly hampered by an incorrect assumption that preserving the soundness of dynamic analysis requires an underlying sound static analysis. We observe that, even with unsound static analysis, it is possible to achieve sound dynamic analysis for the executions which fall within the set of states statically considered. This leads us to a new approach, called optimistic hybrid analysis. We first profile a small set of executions and generate a set of likely invariants that hold true during most, but not necessarily all, executions. Next, we apply a much more precise, but unsound, static analysis that assumes these invariants hold true. Finally, we run the resulting dynamic analysis speculatively while verifying whether the assumed invariants hold true during that particular execution; if not, the program is reexecuted with a traditional hybrid analysis. Optimistic hybrid analysis is as precise and sound as traditional dynamic analysis, but is typically much faster because (1) unsound static analysis can speed up dynamic analysis much more than sound static analysis can and (2) verifications rarely fail. We apply optimistic hybrid analysis to race detection and program slicing and achieve 1.8x over a state-of-the-art race detector (FastTrack) optimized with traditional hybrid analysis and 8.3x over a hybrid backward slicer (Giri).

Many dynamic analysis tools for programs written in managed languages such as Java rely on bytecode instrumentation. Tool development is often tedious because of the use of low-level bytecode manipulation libraries. While aspect-oriented programming (AOP) offers high-level abstractions to concisely express certain dynamic analyses, the join point model of mainstream AOP languages such as AspectJ is not well suited for many analysis tasks and the code generated by weavers in support of certain language features incurs high overhead. In this paper we introduce DiSL (domain-specific language for instrumentation), a new language especially designed for dynamic program analysis. DiSL offers an open join point model where any region of bytecodes can be a shadow, synthetic local variables for efficient data passing, efficient access to comprehensive static and dynamic context information, and weave-time execution of user-defined static analysis code. We demonstrate the benefits of DiSL with a case study, recasting an existing dynamic analysis tool originally implemented in AspectJ. We show that the DiSL version offers better code coverage, incurs significantly less overhead, and eases the integration of new analysis features that could not be expressed in AspectJ.

The paper describes the architecture and implementation of the dynamic analysis tools that are part of theDOBERTSEE environment. DOBERTSEE is a low-cost, flexible software engineering environment which enables different development processes and methods to be supported by integrating different tools. An XML-based language is used as a basis for integration. The current version of the environment supports the HRT-HOOD method and Ravenscar Ada, and includes an extensive set of static and dynamic analysis tools.

Blockchain technology and its applications are gaining popularity day by day. It is a ground-breaking technology that allows users to communicate without the need of a trusted middleman. A smart contract (self-executable code) is deployed on the blockchain and auto executes due to a triggering condition. In a no-trust contracting environment, smart contracts can establish trust among parties. Terms and conditions embedded in smart contracts will be imposed immediately when specified criteria have been fulfilled. Due to this, the malicious assailants have a special interest in smart contracts. Blockchains are immutable means if some transaction is deployed or recorded on the blockchain, it becomes unalterable. Thus, smart contracts must be analyzed to ensure zero security vulnerabilities or flaws before deploying the same on the blockchain because a single vulnerability can lead to the loss of millions. For analyzing the security vulnerabilities of smart contracts, various analysis tools have been developed to create safe and secure smart contracts. This paper presents a systematic review on Ethereum smart contracts analysis tools. Initially, these tools are categorized into static and dynamic analysis tools. Thereafter, different sources code analysis techniques are studied such as taint analysis, symbolic execution, and fuzzing techniques. In total, 86 security analysis tools developed for Ethereum blockchain smart contract are analyzed regardless of tool type and analysis approach. Finally, the paper highlights some challenges and future recommendations in the field of Ethereum smart contracts.

Ethereum smart contracts are computer programs that are deployed and executed on the Ethereum blockchain to enforce agreements among untrusting parties. Being the most prominent platform that supports smart contracts, Ethereum has been targeted by many attacks and plagued by security incidents. Consequently, many smart contract vulnerabilities have been discovered in the past decade. To detect and prevent such vulnerabilities, different security analysis tools, including static and dynamic analysis tools, have been created, but their performance decreases drastically when codes to be analyzed are constantly being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool that detects smart contract vulnerabilities. Eth2Vec maintains its robustness against code rewrites; i.e., it can detect vulnerabilities even in rewritten codes. Other machine-learning-based static analysis tools require features, which analysts create manually, as inputs. In contrast, Eth2Vec uses a neural network for language processing to automatically learn the features of vulnerable contracts. In doing so, Eth2Vec can detect vulnerabilities in smart contracts by comparing the similarities between the codes of a target contract and those of the learned contracts. We performed experiments with existing open databases, such as Etherscan, and Eth2Vec was able to outperform a recent model based on support vector machine in terms of well-known metrics, i.e., precision, recall, and F1-score.

The subject paper discusses important impacts of distributed resources on distribution networks and feeders. These include capacity, line losses, voltage regulation, and central system support (such as volt/var control via central generators and substation) as the number, placement and penetration levels of distributed resources are varied. Typically, the impacts of distributed resources on the distribution system are studied by using steady-state rather than dynamic analysis tools. However, the response time and transient impacts of both system equipment (such as substation/feeder capacitors) and distributed resources needs to be taken into account and only dynamic analysis will provide the full impact results. ORNL is completing a study of distributed resources interconnected to a large distribution system considering the above variables. A report of the study and its results will be condensed into a paper for this panel session. The impact of distributed resources will vary as the penetration level reaches the capacity of the distribution feeder/system. The question is how high of a penetration of distributed resource can be accommodated on the distribution feeder/system without any major changes to system operation, system design and protection. The impacts will most surely vary depending upon load composition, distribution and level. Also, it is expected that various placement of distributed resources will impact the distribution system differently.

Dynamic analysis tools are often implemented using instrumentation, particularly on managed runtimes including the Java Virtual Machine (JVM). Performing instrumentation robustly is especially complex on such runtimes: existing frameworks offer limited coverage and poor isolation, while previous work has shown that apparently innocuous instrumentation can cause deadlocks or crashes in the observed application. This paper describes ShadowVM, a system for instrumentation-based dynamic analyses on the JVM which combines a number of techniques to greatly improve both isolation and coverage. These centre on the offload of analysis to a separate process; we believe our design is the first system to enable genuinely full bytecode coverage on the JVM. We describe a working implementation, and use a case study to demonstrate its improved coverage and to evaluate its runtime overhead.

To ensure the steady motion of underwater supercavitating vehicles, based on a four-dimensional dynamic model, the complex dynamical behaviors of vehicles are analyzed by using dynamic analysis tools, the conditions and ranges of variable parameters for the stable motion of vehicles are determined by two-dimensional bifurcation method. And the motion of supercavitating vehicles changing with feedback control gain of fin deflection angle and cavitation number is explored. The results indicate that the dynamical behaviors of supercavitating vehicles not only depend on system parameters, but also closely depend on initial conditions of the system. It can be found some novel nonlinear phenomenon in the system, such as transient period, transient chaos and state transitions and so on. Vibrations and shocks can be suppressed effectively by adjusting fin deflection angle reasonably. The researches have an important guiding significance to the stability control of supercavitating vehicles.

A novel approach for the dynamic analysis of Lithium-ion batteries is the investigation of higher harmonic voltage responses with Nonlinear Frequency Response Analysis (NFRA). This is commonly applied in the field of acoustics [1], material science [2], for fuel cells [3] and as a state of charge estimator for Lead-Acid batteries [4]. NFRA is an adaption and advancement of Electrochemical Impedance Spectroscopy (EIS). In contrast to EIS, NFRA allows to study nonlinear processes in Lithium-ion batteries, such as electrode reactions and solid diffusion in spherical particles, without linearization. By analyzing the NFRA spectrum and correlating the dynamic responses with the corresponding EIS spectrum, a novel and deeper insight into battery processes can be provided. We will show the application of NFRA on Lithium-ion batteries with a model-based study for optimal identification and quantification of the individual processes. A state-of-the-art Single Particle Doyle-Newman model [5] is extended with a SEI and double layers at each interface and adapted to simulate NFRA of Lithium-ion batteries. In contrary to EIS, the system is excited with a high amplitude guaranteeing nonlinear behavior. The model output in the time domain is transferred via Fast-Fourier-Transformation (FFT) to the frequency domain, thereby accessing the amplitudes of the individual harmonics Y n as well as their sum in dependency of the frequency. Once a system is excited with a sinusoidal input current, it will reach for a dynamic-steady-state. We will show how state variables, e.g. solid concentration and double-layer potentials, contribute to this behavior at characteristic frequencies defined for the NFRA spectrum in Figure 1. Thereby investigating the number of oscillations needed for dynamic-steady-state analysis. With decreasing frequency, ranging from 1131 Hz to 5.3 Hz to 0.3 Hz, the influence of the anode solid concentration csurf,a increases until it becomes dominant in the low frequency region at 0.3 Hz. Further, only the double-layer potentials Δϕ a and Δϕ SEI at the anode side are influenced, indicating the anode as limiting electrode. We further focus on the investigation of the dynamic-steady-state for process identification by parameter and amplitude variations. The impact of the variation of diffusion coefficient, reaction rates and double-layer capacitances is demonstrated. Changes in SEI processes influence the overall excited frequency range, whereas changes in reaction and diffusion processes affect only certain frequency regions. Thus, frequency ranges can be assigned to processes, therefore enabling process identification. Additionally, with a variation of processes, the amplitude dependency changes. Further, individual harmonics show different characteristic progressions within the overall frequency range. Process identification on Lithium-ion batteries in general can highly benefit from these findings. Besides NFRA, we provide EIS spectra, simulated with small excitation amplitudes for linear excitation of the system, for comparison to NFRA simulation results. With NFRA, a powerful new dynamic analysis tool is created, accessing a new dimension to the electrochemical characterization of Lithium-ion batteries. Literature: [1] Everest, F. A., et al., Master Handbook of Acoustics, The McGraw-Hill Companies, 2009. [2] Wilson, J. R., et al., Electrochimica Acta, 51, p. 1389–1402, 2006. [3] Mao, Qing, et al., Electrochemistry Communications, 12, p. 1517-1519, 2010. [4] S. Okazaki, J. Electrochemical Society 132, p. 1516, 1985. [5] Doyle, Marc, et al., J. Electrochem. Soc., 143, (6), p. 1890-1903, 1996. Figure 1