Data Flow Analysis in Software Reliability

Data flow analysis is used widely in program compilation, understanding, design, and analysis tools. In data flow analysis, problem-specific information is associated with nodes and/or edges in the flow graph representation of a program or component and re-comp\-uted iteratively. A popular data flow analysis design relies on a worklist that stores all nodes and edges whose data flow information has to be re-computed. While this approach is straightforward, it has some drawbacks. First, the presence of the worklist makes data flow algorithms centralized, which may reduce effectiveness of parallel implementations of these algorithms. Second, the worklist approach is difficult to implement in a way that minimizes the amount of information passed between flow graph nodes.In this paper, we propose to use the well-known Observer pattern for implementation of data flow analyses. We argue that such implementations are more object-oriented in nature, as well as less centralized, than worklist-based ones. We argue that by adopting this Observer-based view, data flow analyses that minimize the amount of information passed between flow graph nodes can be implemented easier than by using the worklist view. We present experimental data indicating that for some types of data flow problems, even single-threaded implementations of Observer-based data flow analysis have better run times than comparable worklist-based implementations.

Static and dynamic program analysis techniques are important research areas in software security. Static analysis helps us locate vulnerabilities in a software by looking at the source code. Dynamic analysis helps us reason about the behavior of the software from information gathered at run-time. In this thesis, we are focusing on data flow analysis and heap analysis which are key static and dynamic program analysis techniques respectively.
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\nIn the first part of this thesis, we aim at detecting vulnerabilities in Android applications which have capability leaks. The security of the Android platform relies mainly on sandboxing applications and restricting their capabilities such that no application, by default, can perform any operations that would adversely impact other applications, the operating system, or the user. However, a recent research reported that a genuine but vulnerable application may leak its capabilities. When being leveraged, other applications can gain extra capabilities which they are not granted originally. We present DroidChecker, an Android application analyzing tool which searches for the aforementioned vulnerability in Android applications. DroidChecker uses interprocedural control flow graph searching and static taint checking to detect exploitable data paths in an Android application. We analyzed more than 1100 Android applications using DroidChecker and found 6 previously unknown vulnerable applications including the renowned Adobe Photoshop Express application. We also developed a malicious application that exploits the previously unknown vulnerability found in the Adobe Photoshop Express application. We showed that the malicious application, which is not granted any permissions, can access contacts on the phone with just a few lines of code.
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\nIn the second part of this thesis, we explore the use of heap analysis to extract software birthmarks. There are techniques like code obfuscation and watermarking which can make the source code of a program difficult to understand by humans and prove the ownership of the program. However, code obfuscation cannot avoid the source code being copied and a watermark can be defaced. A birthmark is a group of unique characteristics a program possesses that can be used to identify the program. We propose two novel dynamic birthmark systems based on the run-time heap. A dynamic birthmark is one that is extracted when the program is executing. Since it is based on the run-time behavior of the program, semantics-preserving transformations of the code like obfuscation cannot defeat dynamic birthmarks. In this regard, dynamic birthmarks are more robust compared with static birthmarks.
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\nTo the best of our knowledge, these are the first birthmark systems using heap analysis as the underlying technique. The basic idea is to take snapshots of the heap while the program is running. From the snapshots, heap graphs are constructed to model the referencing structure between objects. After going through some filtering and referencing processes, they become the birthmarks. The two birthmark systems have been devised to extract birthmarks for Java programs and JavaScript programs respectively. While the underlying ideas of the two birthmark systems are similar, the differences in nature of the two programming languages led to different implementation designs.

A number of coverage criteria have been proposed for testing classes and class clusters modeled with state machines. Previous research has revealed their limitations in terms of their capability to detect faults. As these criteria can be considered to execute the control flow structure of the state machine, we are investigating how data flow information can be used to improve them in the context of UML state machines. More specifically, we investigate how such data flow analysis can be used to further refine the selection of a cost‐effective test suite among alternative, adequate test suites for a given state machine criterion. This paper presents a comprehensive methodology to perform data flow analysis of UML state machines—with a specific focus on identifying the data flow from OCL guard conditions and operation contracts—and applies it to a widely referenced coverage criterion, the round‐trip path (transition tree) criterion. It reports on two case studies whose results show that data flow information can be used to select the best transition tree, in terms of cost effectiveness, when more than one satisfies the transition tree criterion. The results also suggest that different trees are complementary in terms of the data flow that they exercise, thus, leading to the detection of intersecting but distinct subsets of faults. Copyright © 2009 John Wiley & Sons, Ltd.

Analysis and mapping of national data flows and processes relevant to EFSA's business data collections is a long‐standing EFSA objective, originally launched with EFSA's SIGMA project in 2018‐2019 and further continued with the launch of a focused activity to “Develop, pilot and document a methodology for the data flow mapping and analysis in the European Member States” in 2023. The aim of latter activity is to apply a generic methodology for the analysis of any business data collection at Member State level with the ultimate purpose for countries and EFSA to increase understanding of data flows at national level, where existing challenges rely on and what common solutions could be implemented. This report presents results from the execution of the aforementioned methodology on Norway for the analysis of national data flows which contribute data to Food Consumption business data collection. The report contains information on the steps, participant organizations and actors, tools, systems, formats, structural and content metadata, as well as challenges and “pain points” identified in the country's internal data flows related to the collection of the selected data. The presented information has been generated based on the responses submitted by the participating Member State to a survey developed in the context of the data flow mapping and analysis methodology as well as relevant discussions held in a post‐survey interview with the country. Though the information presented on the current report represents real‐world data, it must be clarified that the survey and the follow‐up interview with the MS, have been both performed during the activity of the Data Flow Mapping methodology.

In this paper, we propose an efficient techniques, called CD translation, to compile a FORTRAN program to a optimized dataflow code. The CD translation generates the dataflow control information from a control flow graph by using the data flow analysis with the branch node operation, and enables to analyze a sequential program with any type of the control structures (e.g. goto statement and irreducible loop) correctly, while the previous method cannot compile a FORTRAN to the dataflow program perfectly. This analysis technique is much worthy of not only the construction of the compiler for dataflow machines but also the analysis technique for the parallelizing compiler because the dataflow program represents all the program dependencies with only data dependencies unifyingly and enables to analyze all the program dependencies identically. Moreover the FORTRAN compiler implementing the CD translation is introduced.Index Termsdataflow machinecompilerdataflow analysisFORTRAN

Parallel data flow analysis methods offer the promise of calculating detailed semantic information about a program at compile-time more efficiently than sequential techniques. Previous work on parallel elimination methods has been hampered by the lack of control over interval size; this can prohibit effective parallel execution of these methods. To overcome this problem, we have designed the region analysis method, a new elimination method for data flow analysis. Region analysis emphasizes flow graph partitioning to enable better load balancing in a more effective parallel algorithm. We present the design of region analysis and the empirical results we have obtained that indicate: the prevalence of large intervals in flow graphs derived from real programs; and the performance improvement of region analysis over parallel Allen-Cocke interval analysis. Our implementation analyzed programs from the Perfect Benchmarks and netlib running on a Sequent Symmetry S81. >

Data flow is the name applied when data are passed from one thing to another. The “thing” that passes the data can include a piece of software or an application, a computer or a region in a computer, a person, group, or organizational unit, or combinations of these. The data passed can be simple or complex and it can be passed as a stream or a unit. A diagram that depicts objects or processes and the possible flows of data among them is called a data flow diagram (DFD). DFDs are useful for depicting information about how an organization operates; the interfaces between an application and the people or other applications that use it; and the high‐level design of an application. DFDs have been used in application development for a long time. There are two alternatives to (pure) data flow and each alternative has substantial disadvantages when used to link software components. One of these alternatives to data flow is for the data not to flow; that is, have various software modules access it directly in shared common data areas. A second alternative to pure data flow is the “call” statement, used in most languages to link subroutines. The call statement has two substantial limitations versus pure data flow: it also passes control and it explicitly names the target subroutine. In today's application development methodologies, DFDs are important for: analyzing organizational units; depicting the context of an application relative to users and other applications; designing the high level (process) architecture of applications; showing the interfaces between application processes and data stores; and documenting batch job streams. DFDs are likely to be increasingly important for designing distributed systems, object‐oriented design, depicting asynchronous components, and for reusable code. An examination of each is given.

It is known that not all paths are possible in the run time control flow of many programs. It is also known that data flow analysis cannot restrict attention to exactly those paths that are possible. It is therefore usual for analytic methods to consider all paths. Sharper information can be obtained by considering a recursive set of paths that is large enough to include all possible paths but small enough to exclude many of the impossible ones. This paper presents a simple uniform methodology for sharpening data flow information by considering certain recursive path sets of practical importance. Associated with each control flow arc there is a relation on a finite set Q. The paths that qualify to be considered are (essentially) those for which the composition of the relations encountered is nonempty. For example, Q might be the set of all assignments of values to each of several bit variables used by a program to remember some facts about the past and branch accordingly in the future. Given any data flow problem together with qualifying relations on Q associated with the control flow arcs, we construct a new problem. Considering all paths in the new problem is equivalent to considering only qualifying paths in the old one. Preliminary experiments (with a small set of real programs) indicate that qualified analysis is feasible and substantially more informative than ordinary analysis. The methodology also has a beneficial feedback effect on the delicate task of passing from programs to meaningful data flow analysis problems. Even when all paths qualify, unusually sharp information can be obtained by passing from programs to problems in ways suggested by our theorems.

In this paper, we propose a software vulnerability checker which takes rules describing vulnerability patterns and a source program as input and detects locations and paths of the patterns in the program. Simple and flow patterns for vulnerabilities are described as rules in the specification language we designed. The lightweight control and data flow analysis is necessary to detect flow patterns. Newly discovered vulnerability patterns can easily be added to the existing rules. We implement the detector in three parts: a pattern matcher which finds locations of vulnerabilities in source program, a flow graph constructor which extracts the control flow and data flow from the program, and a flow analyzer which finds program's vulnerable execution paths.

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.

It is known that not aU paths are possible in the run time control flow of many programs. It is also known that data flow analysis cannot restrict attention to exactly those paths that are possible. It is, therefore, usual for analytic methods to consider aU paths. Sharper information can be obtained by considering a recursive set of paths that is large enough to include aUl possible paths, but smaU enough to exclude many of the impossible ones. This paper presents a simple uniform methodology for sharpening data flow information by considering certain recursive path sets of practical importance. Associated with each control flow arc there is a relation on a finite set Q. The paths that qualify to be considered are (essentially) those for which the composition of the relations encountered is nonempty. For example, Q might be the set of all assignments of values to each of several bit variables used by a program to remember some facts about the past and branch accordingly in the future. Given any data-flow problem together with qualifying relations on Q associated with the control flow arcs, we construct a new problem. Considering all paths in the new problem is equivalent to considering only qualifying paths in the old one. Preliminary experiments (with a smaUl set of real programs) indicate that qualified analysis is feasible and substantialy more informative than ordinary analysis.

Reducing communication overhead is extremely important for parallelizing compiler to generate efficient codes for distributed memory machines. In this paper, a redundant parallel execution model (RPEM) is used as the model for target programs. The extend data flow graph is introduced, and optimization algorithms based on the data-flow analysis are discussed. The overhead of data flow analysis can be reduced by performing analysis on the extend dataflow graph. The analysis helps to reduce the redundant communication overhead. These optimization algorithms are able to perform inter-loop and inter-procedure analysis. Experimental results prove that these optimizations algorithms are effective in reducing both the number of communications and the communication volume.

In recent years, the number of Internet of Things and Internet of Everything (IOT/IOE) paradigms has increased significantly. The large number of devices contributed to generate a huge amount of data (Big Data) inserted in Smart City solutions, which are experiencing an explosion of complexity, also due to the increment of protocols, formats and providers. In this perspective it becomes essential to create a data indexing infrastructure that can optimize the performance of the system itself, for creating the so called data shadowing on IOT and other data on cloud. Therefore, it is fundamental to study paradigms to manage the indexing and visual analytics a great variety of data including IOT/IOE. One of the important aspects to be addressed for managing data in the smart city context are: the uniform model, the performance and scalability, response times in research, and the possibilities of performing visual analytic such as data flow analysis and drill down. All these needs imply the creation of a Smart Solution capable of managing and analysing heterogeneous kinds of data, providing a multitude of final applications based on the type of user who requires a certain service. To this end, in this paper, a unified model for IOT/IOE and data ingestion is presented. In addition, two possible architectural solutions have been implemented and compared in terms of performance, resource consumption, reliability and visual analytic tools for data flow. The solutions proposed for data indexing and shadowing have been tested in the context of Snap4City pilot Helsinki and Antwerp for smart city of EC project Select4Cities.

Remote process control and supervision applications developed over the Internet require special communication models and techniques, which can guarantee the real-time and safety restrictions inherent to automation systems. This paper presents a communication model based on dataflow analysis and bandwidth reservation that offers a good control over the transmission time of critical data. As part of the model, an analytical method is proposed that allows a-priory evaluation of the required minimum bandwidth necessary to assure the fulfillment of real-time transmission restrictions. The model is based on the QoS facilities offered by the IPv6 protocol.

An experimental data flow analyzer for PL/I programs has been implemented within the PROBE system developed at the GM Research Laboratories. PROBE is an experimental software package that examines the internal structure of PL/I programs in order to expose error-prone design and programming features. This paper describes 1) the algorithms and data structures used by the data flow analyzer, 2) the salient aspects of PL/I usage in the analyzed production-level programs, and 3) the results of the data flow analysis.