Extracting library-based object-oriented applications

This research investigates numerically the potential failure of different framed structures after removal of a corner column. This study is directed towards the role of the joint above the removed column in terms of framed structures stability and internal forces redistribution utilizing linear static, nonlinear static and nonlinear dynamic analysis procedures. First, the preliminary linear static analysis as a part of more detailed nonlinear static and dynamic analysis to model reinforced concrete frames is performed. For nonlinear numerical models, three different moment-rotation capacity curves are considered for the joint above the removed column. These curves are taken according to FEMA 356 regulation according to the reinforcement condition at the joint location to represent normal joint, anchorage deficient joint and shear deficient joint. In linear static models, the results confirmed that, the more moment the joint can resist at the removed column location, the less moment is generated at the beam’s other end. In nonlinear static analysis, the model with normal plastic hinge properties recorded a higher carrying capacity than the model with both shear and anchorage plastic hinges. Nonlinear dynamic analysis with less assumptions offer the more representative modeling for the considered case study which is obvious from its closer perdition of internal forces and deflection compared to the reference findings.

Detecting performance issues due to suboptimal code during the development process can be a daunting task, especially when it comes to localizing them after noticing performance degradation after deployment. Static analysis has the potential to provide early feedback on performance problems to developers without having to run profilers with expensive (and often unavailable) performance tests. We develop a VSCode tool that integrates the static performance analysis results from Infer via code annotations and decorations (surfacing complexity analysis results in context) and side panel views showing details and overviews (enabling explainability of the results). Additionally, we design our system for interactivity to allow for more responsiveness to code changes as they happen. We evaluate the efficacy of our tool by measuring the overhead that the static performance analysis integration introduces in the development workflow. Further, we report on a case study that illustrates how our system can be used to reason about software performance in the context of a real performance bug in the ElasticSearch open-source project. Demo video: https://www.youtube.com/watch?v=-GqPb_YZMOs Repository: https://github.com/ipa-lab/vscode-infer-performance

Microservice Architecture (MSA) is becoming the predominant direction of new cloud-based applications. There are many advantages to using microservices, but also downsides to using a more complex architecture than a typical monolithic enterprise application. Beyond the normal poor coding practices and code smells of a typical application, microservice-specific code smells are difficult to discover within a distributed application setup. There are many static code analysis tools for monolithic applications, but tools to offer code-smell detection for microservice-based applications are lacking. This paper proposes a new approach to detect code smells in distributed applications based on microservices. We develop an MSANose tool to detect up to eleven different microservice specific code smells and share it as open-source. We demonstrate our tool through a case study on two robust benchmark microservice applications and verify its accuracy. Our results show that it is possible to detect code smells within microservice applications using bytecode and/or source code analysis throughout the development process or even before its deployment to production.

Secure software engineering has emerged in recent decades by encouraging the idea of software security has to be an integral part of all the phases of the software development lifecycle. As a result, each phase of the lifecycle is associated with security-specific best practices such as threat modeling and static code analysis. It was observed that various artifacts (i.e., security requirements, architectural flaws, bug reports, security test cases) generated as a result of security best practices tend to be segregated. This creates a significant barrier to resolve the security issues at the implementation phase since most of them are originated in the design phase. In order to address this issue, this paper presents a knowledge-modeling based approach to semantically infer the associations between architectural level security flaws and code-level security bugs, which is manually tedious. Threat modeling and static analysis are used to identify security flaws and security bugs, respectively. The case study based experimental results revealed that the architectural level security flaws have a significant impact on originating security bugs in the code level. Besides, the evaluation results confirmed the scalability of the proposed approach to large-scale industrial software products.

As a measure of software quality, current static code analyses reveal thousands of quality defects on systems in brown-field development in practice. Currently, there exists no way to prioritize among a large number of quality defects and developers lack a structured approach to address the load of refactoring. Consequently, although static analyses are often used, they do not lead to actual quality improvement. Our approach recommends to remove quality defects, exemplary code clones and long methods, which are easy to refactor and, thus, provides developers a first starting point for quality improvement. With an empirical industrial Java case study, we evaluate the usefulness of the recommendation based on developers’ feedback. We further quantify which external factors influence the process of quality defect removal in industry software development.

To reduce the verification costs and to be more confident on software, static program analysis offers ways to prove properties on source code. Unfortunately, these techniques are difficult to apprehend and to use for non-specialists. Modelling allows users to specify some aspects of software in an easy way. More precisely, in embedded software, state machine models are frequently used for behavioural design. The aim of this paper is to bridge the gap between model and code by offering automatic generation of annotations from model to source code. These annotations are then verified by static analysis in order to ensure that the code behaviour conforms to the model-based design. The models we consider are UML state machines with a formal non-ambiguous semantics, the annotation generation and verification is implemented in a tool and applied to a case study.

One of the inherent advantages of static analysis is that it can create and reason about models of an entire program. However, mainstream languages such as Java use numerous dynamic language features designed to boost programmer productivity, but these features are notoriously difficult to capture by static analysis, leading to unsoundness in practice. While existing research has focused on providing sound handling for selected language features (mostly reflection) based on anecdotal evidence and case studies, there is little empirical work to investigate the extent to which particular features cause unsoundness of static analysis in practice. In this paper, we (1) discuss language features that may cause unsoundness and (2) discuss a methodology that can be used to check the (un)soundness of a particular static analysis, call-graph construction, based on soundness oracles. These oracles can also be used for hybrid analyses.

Android Operating System becomes a major target for malicious attacks. Static analysis approach is widely used to detect malicious applications. Most of existing studies on static analysis frameworks are limited to certain features. This paper presents an Android Static Analysis Framework (ASAF) which models the overall static analysis phases and approaches for Android applications. ASAF can be implemented for different purposes including Android malicious apps detection. The proposed framework utilizes a parsing tool, Android Static Parse (ASParse) which is also introduced in this paper. Through the extendibility of the ASParse tool, future research studies can easily extend the parsed features and the parsed files to perform parsing based on their specific requirements and goals. Moreover, a case study is conducted to illustrate the implementation of the proposed ASAF.

Our research objective is to facilitate the identification of true input manipulation vulnerabilities via the combination of static analysis, runtime detection, and automatic testing. We propose an approach for SQL injection vulnerability detection, automated by a prototype tool SQLInjectionGen. We performed case studies on two small Web applications for the evaluation of our approach compared to static analysis for identifying true SQL injection vulnerabilities. In our case study, SQLInjectionGen had no false positives, but had a small number of false negatives while the static analysis tool had a false positive for every vulnerability that was actually protected by a white or black list.

Eliminating vulnerabilities from low-level code is vital for securing software. Static analysis is a promising approach for discovering vulnerabilities since it can provide developers early feedback on the code they write. But, it presents multiple challenges not the least of which is understanding what makes a bug exploitable and conveying this information to the developer. In this paper, we present the design and implementation of a practical vulnerability assessment framework, called [InlineEquation not available: see fulltext.]. Melange performs data and control flow analysis to diagnose potential security bugs, and outputs well-formatted bug reports that help developers understand and fix security bugs. Based on the intuition that real-world vulnerabilities manifest themselves across multiple parts of a program, Melange performs both local and global analyses in stages. To scale upi?źto large programs, global analysis is demand-driven. Our prototype detects multiple vulnerability classes in C and C++ code including type confusion, and garbage memory reads. We have evaluated Melange extensively. Our case studies show that Melange scales upi?źto large codebases such as Chromium, is easy-to-use, and most importantly, capable of discovering vulnerabilities in real-world code. Our findings indicate that static analysis is a viable reinforcement to the software testing tooli?źset.

Traditional methods rely on Static Timing Analysis techniques to compute the Worst Case Response Time for tasks in real-time systems. Multi-Core real-time systems are faced up with concurrent task executions, semaphore accesses, and task migrations where it may be difficult to obtain the worst case upper bound. A new three staged probabilistic estimation concept is presented. Worst Case Response Times are estimated for tasksets which consist of tasks with multiple time bases. The concept involves data generation with sample classification and sample size equalization, model fit and Worst Case Response Time estimation on the basis of extreme value distribution models. A Generalized Pareto Distribution model fit method which includes threshold detection and parameter estimation is also presented. Sample classification in combination with the new Generalized Pareto Distribution model fit method allows to estimate Worst Case Response Times with low pessimism ranges compared to estimation methods that uses the Generalized Pareto or the Gumbel max distribution without sample classification.

It is well known that a staged interpreter is a compiler: specializing an interpreter to a given program produces an equivalent executable that runs faster. This connection is known as the first Futamura projection. It is even more widely known that an abstract interpreter is a program analyzer: tweaking an interpreter to run on abstract domains produces a sound static analysis. What happens when we combine these two ideas, and apply specialization to an abstract interpreter? In this paper, we present a unifying framework that naturally extends the first Futamura projection from concrete interpreters to abstract interpreters. Our approach derives a sound staged abstract interpreter based on a generic interpreter with type-level binding-time abstractions and monadic abstractions. By using different instantiations of these abstractions, the generic interpreter can flexibly behave in one of four modes: as an unstaged concrete interpreter, a staged concrete interpreter, an unstaged abstract interpreter, or a staged abstract interpreter. As an example of abstraction without regret, the overhead of these abstraction layers is eliminated in the generated code after staging. We show that staging abstract interpreters is practical and useful to optimize static analysis, all while requiring less engineering effort and without compromising soundness. We conduct three case studies, including a comparison with Boucher and Feeley’s abstract compilation, applications to various control-flow analyses, and a demonstration for modular analysis. We also empirically evaluate the effect of staging on the execution time. The experiment shows an order of magnitude speedup with staging for control-flow analyses.

The specification language is a critical component of the hardware-software co-design process since it is used for functional validation and as a starting point for hardware-software partitioning and co-synthesis. The paper proposes the Java programming language as a specification language for hardware-software systems. Java has several characteristics that make it suitable for system specification. However static control and data flow analysis of Java programs is problematic because Java classes are dynamically linked. The paper provides a general solution to the problem of statically analyzing Java programs using a technique that pre-allocates most class instances and aggressively resolves memory aliasing using global analysis. The output of the analysis is a control data flow graph for the input specification. The results for sample designs show that the analysis can extract fine to coarse-grained concurrency for subsequent hardware-software partitioning and co-synthesis steps of the hardware-software co-design process to exploit.

This talk will try to frame JML in two senses. The first is the sense of placing JML in the context of other specification languages and tools. This context will be provided by giving a brief introduction to JML, with small examples. The different tools that work with JML also help provide context for the language design in a different way. Another aspect of this context is provided by comparing JML with other design by contract languages, such as Eiffel.The second sense of framing in JML is the more technical one of frame axioms, also known as modifies clauses. Such frame axioms are critical for reasoning, and are difficult to check at runtime; hence static analysis is a crucial need. We will discuss the semantics of JML's frame axioms, including datagroups, both in a naive sense and with the experimental Universe type system. Both problems in the semantics and checking, as well as some recent work will be presented. Note that some of the recent work on frame axioms in JML, especially extensions with the Universe type system, is joint work with Peter Muller (of ETH Zurich) and Arnd Poetzsch-Heffter of (U. Kaiserslautern). At Iowa State, the development of JML was partially funded by the (US) National Science Foundation under grants CCR-9503168, CCR-9803843, CCR-0097907, and CCR-0113181.

A review of static and dynamic analysis of ball screw feed drives, recirculating linear guideway, and ball screw