Trace Slicing Research Articles

A Story of Parametric Trace Slicing, Garbage and Static Analysis

This paper presents a proposal (story) of how statically detecting unreachable objects (in Java) could be used to improve a particular runtime verification approach (for Java), namely parametric trace slicing. Monitoring algorithms for parametric trace slicing depend on garbage collection to (i) cleanup data-structures storing monitored objects, ensuring they do not become unmanageably large, and (ii) anticipate the violation of (non-safety) properties that cannot be satisfied as a monitored object can no longer appear later in the trace. The proposal is that both usages can be improved by making the unreachability of monitored objects explicit in the parametric property and statically introducing additional instrumentation points generating related events. The ideas presented in this paper are still exploratory and the intention is to integrate the described techniques into the MarQ monitoring tool for quantified event automata.

Inferring software behavioral models with MapReduce

In the real world practice, software systems are often built without developing any explicit upfront model. This can cause serious problems that may hinder the almost inevitable future evolution, since at best the only documentation about the software is in the form of source code comments. To address this problem, research has been focusing on automatic inference of models by applying machine learning algorithms to execution logs. However, the logs generated by a real software system may be very large and the inference algorithm can exceed the processing capacity of a single computer. This paper proposes a scalable, general approach to the inference of behavior models that can handle large execution logs via parallel and distributed algorithms implemented using the MapReduce programming model and executed on a cluster of interconnected execution nodes. The approach consists of two distributed phases that perform trace slicing and model synthesis . For each phase, a distributed algorithm using MapReduce is developed. With the parallel data processing capacity of MapReduce, the problem of inferring behavior models from large logs can be efficiently solved. The technique is implemented on top of Hadoop. Experiments on Amazon clusters show efficiency and scalability of our approach. • A distributed trace slicing algorithm using MapReduce. • A distributed model synthesis algorithm using MapReduce. • A novel approach for inferring software behavior models with MapReduce. • Experimental results show promising performance of this approach.

Debugging Maude programs via runtime assertion checking and trace slicing

In this paper we propose a dynamic analysis methodology for improving the diagnosis of erroneous Maude programs. The key idea is to combine runtime checking and dynamic trace slicing for automatically catching errors at runtime while reducing the size and complexity of the erroneous traces to be analyzed (i.e., those leading to states failing to satisfy some of the assertions). First, we formalize a technique that is aimed at automatically detecting deviations of the program behavior (symptoms) with respect to two types of user-defined assertions: functional assertions and system assertions. The proposed dynamic checking is provably sound in the sense that all errors flagged are definitely violations of the specifications. Then, upon eventual assertion violations we generate accurate trace slices that help identify the cause of the error. Our methodology is based on (i) a logical notation for specifying assertions that are imposed on execution runs; (ii) a runtime checking technique that dynamically tests the assertions; and (iii) a mechanism based on (equational) least general generalization that automatically derives accurate criteria for slicing from falsified assertions. Finally, we report on an implementation of the proposed technique in the assertion-based, dynamic analyzer ABETS and show how the forward and backward tracking of asserted program properties leads to a thorough trace analysis algorithm that can be used for program diagnosis and debugging.

Using conditional trace slicing for improving Maude programs

Understanding the behavior of software is important for the existing software to be improved. In this paper, we present a trace slicing technique that is suitable for analyzing complex, textually-l...

A core calculus for provenance

Provenance is an increasing concern due to the ongoing revolution in sharing and processing scientific data on the Web and in other computer systems. It is proposed that many computer systems will need to become provenance-aware in order to provide satisfactory accountability, reproducibility, and trust for scientific or other high-value data. To date, there is not a consensus concerning appropriate formal models or security properties for provenance. In previous work, we introduced a formal framework for provenance security and proposed formal definitions of properties called disclosure and obfuscation. In this article, we study refined notions of positive and negative disclosure and obfuscation in a concrete setting, that of a general-purpose programing language. Previous models of provenance have focused on special-purpose languages such as workflows and database queries. We consider a higher-order, functional language with sums, products, and recursive types and functions, and equip it with a tracing semantics in which traces themselves can be replayed as computations. We present an annotation-propagation framework that supports many provenance views over traces, including standard forms of provenance studied previously. We investigate some relationships among provenance views and develop some partial solutions to the disclosure and obfuscation problems, including correct algorithms for disclosure and positive obfuscation based on trace slicing.

Using conditional trace slicing for improving Maude programs

Understanding the behavior of software is important for the existing software to be improved. In this paper, we present a trace slicing technique that is suitable for analyzing complex, textually-large computations in rewriting logic, which is a general framework efficiently implemented in the Maude language that seamlessly unifies a wide variety of logics and models of concurrency. Given a Maude execution trace T and a slicing criterion for the trace (i.e., a piece of information that we want to observe in the final computation state), we traverse T from back to front and the backward dependence of the observed information is incrementally computed at each execution step. At the end of the traversal, a simplified trace slice is obtained by filtering out all the irrelevant data that do not impact on the data of interest. By narrowing the size of the trace, the slicing technique favors better inspection and debugging activities since most tedious and irrelevant inspections that are routinely performed during diagnosis and bug localization can be eliminated automatically. Moreover, cutting down the execution trace can expose opportunities for further improvement, which we illustrate by means of several examples that we execute by using iJulienne, a trace slicer that implements our conditional slicing technique and is endowed with a trace querying mechanism that increases flexibility and reduction power.

Using passive testing based on symbolic execution and slicing techniques: Application to the validation of communication protocols

This paper presents a new approach to perform passive testing based on the analysis of the control and data part of the system under test. Passive testing techniques are based on the observation and verification of properties on the behaviour of a system without interfering with its normal operation. Many passive testing techniques consider only the control part of the system and neglect data, or are confronted with an overwhelming amount of data values to process. In our approach, we consider control and data parts by integrating the concepts of symbolic execution and we improve trace analysis by introducing trace slicing techniques. Properties are described using Input–Output Symbolic Transition Systems (IOSTSs) and we illustrate in the paper how they can be tested on real execution traces optimizing the trace analysis. These properties can be designed to test the functional conformance of a protocol as well as security properties.In addition to the theoretical approach, we have developed a software tool that implements the algorithms presented in this paper. Finally, as a proof of concept of our approach and tool we have applied the techniques to a real-life case study: the SIP protocol. In particular, the proposed techniques are applied to a set of real execution traces extracted from an IMS/SIP architecture.

Functional programs that explain their work

We present techniques that enable higher-order functional computations to "explain" their work by answering questions about how parts of their output were calculated. As explanations, we consider the traditional notion of program slices, which we show can be inadequate, and propose a new notion: trace slices. We present techniques for specifying flexible and rich slicing criteria based on partial expressions, parts of which have been replaced by holes. We characterise program slices in an algorithm-independent fashion and show that a least slice for a given criterion exists. We then present an algorithm, called unevaluation , for computing least program slices from computations reified as traces. Observing a limitation of program slices, we develop a notion of trace slice as another form of explanation and present an algorithm for computing them. The unevaluation algorithm can be applied to any subtrace of a trace slice to compute a program slice whose evaluation generates that subtrace. This close correspondence between programs, traces, and their slices can enable the programmer to understand a computation interactively, in terms of the programming language in which the computation is expressed. We present an implementation in the form of a tool, discuss some important practical implementation concerns and present some techniques for addressing them.

Semantics and Algorithms for Parametric Monitoring

Analysis of execution traces plays a fundamental role in many program analysis approaches, such as runtime verification, testing, monitoring, and specification mining. Execution traces are frequently parametric, i.e., they contain events with parameter bindings. Each parametric trace usually consists of many meaningful trace slices merged together, each slice corresponding to one parameter binding. This gives a semantics-based solution to parametric trace analysis. A general-purpose parametric trace slicing technique is introduced, which takes each event in the parametric trace and dispatches it to its corresponding trace slices. This parametric trace slicing technique can be used in combination with any conventional, non-parametric trace analysis technique, by applying the later on each trace slice. As an instance, a parametric property monitoring technique is then presented. The presented parametric trace slicing and monitoring techniques have been implemented and extensively evaluated. Measurements of runtime overhead confirm that the generality of the discussed techniques does not come at a performance expense when compared with existing parametric trace monitoring systems.

Reticella: An Execution Trace Slicing and Visualization Tool Based on a Behavior Model

Reticella: An Execution Trace Slicing and Visualization Tool Based on a Behavior Model