Sequences Of Method Invocations Research Articles

Embedding and classifying test execution traces using neural networks

Classifying test executions automatically as pass or fail remains a key challenge in software testing and is referred to as the test oracle problem. It is being attempted to solve this problem with supervised learning over test execution traces. A programme is instrumented to gather execution traces as sequences of method invocations. A small fraction of the programme's execution traces is labelled with pass or fail verdicts. Execution traces are then embedded as fixed length vectors and a neural network (NN) component that uses the line-by-line information to classify traces as pass or fail is designed. The classification accuracy of this approach is evaluated using subject programs from different application domains—1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Nine network protocols from Linux packet identifier, L7-Filter and 6. Utilities library, commons-lang for Java. For all subject programs, it was found that test execution classification had high precision, recall and specificity, averaging to 93%, 94% and 96%, respectively, while only training with an average 14% of the total traces. Experiments show that the proposed NN-based approach is promising in classifying test executions from different application domains.

Synthesizing replacement classes

We present a new technique for automatically synthesizing replacement classes. The technique starts with an original class O and a potential replacement class R, then uses R to synthesize a new class that implements the same interface and provides the same functionality as O. Critically, our technique works with a synthe- sized inter-class equivalence predicate between the states of O and R. It uses this predicate to ensure that original and synthesized methods leave corresponding O and R objects in equivalent states. The predicate therefore enables the technique to synthesize individual replacement methods in isolation while still obtain- ing a replacement class that leaves the original and replacement objects in equivalent states after arbitrarily long method invocation sequences. We have implemented the technique as part of a tool, named Mask, and evaluated it using open-source Java classes. The results highlight the effectiveness of Mask in synthesizing replacement classes.

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

An effective long string searching algorithm towards component security testing

In the execution of method invocation sequences to test component security, abnormal or normal information is generated and recorded in a monitor log. By searching abnormal information from monitor log, the exceptions that the component has can be determined. To facilitate the searching process, string searching methods could be employed. However, current approaches are not effective enough to search long pattern string. In order to mine the specific information with less number of matches, we proposed an improved Sunday string searching algorithm in this paper. Unlike Sunday algorithm which does not make use of the already matched characters, the proposed approach presents two ideas — utilizing and recycling these characters. We take advantage of all matched characters in main string, if they are still in the matchable interval compared with pattern string, to increase the distance that pattern string moves backwards. Experimental analysis shows that, compared to Sunday algorithm, our method could greatly reduce the matching times, if the scale of character set constituting both main string and pattern string is small, or if the length of pattern string is long. Also, the proposed approach can improve the search effectiveness for abnormal information in component security testing.

Symbolic automata for representing big code

Analysis of massive codebases (big code) presents an opportunity for drawing insights about programming practice and enabling code reuse. One of the main challenges in analyzing big code is finding a representation that captures sufficient semantic information, can be constructed efficiently, and is amenable to meaningful comparison operations. We present a formal framework for representing code in large codebases. In our framework, the semantic descriptor for each code snippet is a partial temporal specification that captures the sequences of method invocations on an API. The main idea is to represent partial temporal specifications as symbolic automata--automata where transitions may be labeled by variables, and a variable can be substituted by a letter, a word, or a regular language. Using symbolic automata, we construct an abstract domain for static analysis of big code, capturing both the partialness of a specification and the precision of a specification. We show interesting relationships between lattice operations of this domain and common operators for manipulating partial temporal specifications, such as building a more informative specification by consolidating two partial specifications, and comparing partial temporal specifications.

Verification of Architectural Constraints on Sequences of Method Invocations

The importance of correspondence between the architectural prescription and implementation has been long recognized. This paper presents an approach to verification of constraints on method invocation chains prescribed by an architectural style. It consists of two key steps. One, static backward and forward search is applied on the call graph of the system, to find all potential paths between the initial method and the final method prescribed in the architecture. Two, symbolic execution is applied to check the feasibility of those potential paths and generate tests for all feasible ones to check the correspondence. We implement our approach in a prototype based on Soot and Symbolic PathFinder (SPF), and demonstrate the usefulness of our approach using a case study.

Procedure-modular specification and verification of temporal safety properties

This paper describes ProMoVer, a tool for fully automated procedure-modular verification of Java programs equipped with method-local and global assertions that specify safety properties of sequences of method invocations. Modularity at the procedure-level is a natural instantiation of the modular verification paradigm, where correctness of global properties is relativized on the local properties of the methods rather than on their implementations. Here, it is based on the construction of maximal models for a program model that abstracts away from program data. This approach allows global properties to be verified in the presence of code evolution, multiple method implementations (as arising from software product lines), or even unknown method implementations (as in mobile code for open platforms). ProMoVer automates a typical verification scenario for a previously developed tool set for compositional verification of control flow safety properties, and provides appropriate pre- and post-processing. Both linear-time temporal logic and finite automata are supported as formalisms for expressing local and global safety properties, allowing the user to choose a suitable format for the property at hand. Modularity is exploited by a mechanism for proof reuse that detects and minimizes the verification tasks resulting from changes in the code and the specifications. The verification task is relatively light-weight due to support for abstraction from private methods and automatic extraction of candidate specifications from method implementations. We evaluate the tool on a number of applications from the domains of Java Card and web-based application.

Compositional Mining of Multiple Object API Protocols through State Abstraction

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments.

A CASE Tool for Java Mobile Computing Applications

The market for applications developed for mobile devices is growing as the hardware capabilities increase while costs drop. At the same time, the inability to write code once becomes a noticeable problem resulting in the time consuming task of porting an application to a variety of mobile devices, which is true for languages like Java that have been designed to be portable across a range of hardware platforms. As a result, porting a Java application written for one device to another is often a tedious and time-consuming task for developers. This paper presents an intelligent CASE tool that assists the porting of Java mobile applications by automatically generating Java code fragments for the target SDK. SeqFinder automatically generates all minimal method invocation sequences that lead to an object of a specific type, thus relieving the programmer of manually searching the manufacturer-provided SDK Java archives. However, this tool is not applicable only to the Java ME platform and has been used as a fast type-browser for J2SE/J2EE applications.

Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

Interface Grammars for Modular Software Model Checking

Verification techniques relying on state enumeration (e.g., model checking) face two important challenges: the state-space explosion, an exponential increase in the state space as the number of components increases; and environment generation, modeling components that are either not available for analysis, or that cannot be handled by the verification tool in use. We propose a semi-automated approach for attacking these problems. In our approach, interfaces for the components not under analysis are specified using a specification language based on grammars. Specifically, an interface grammar for a component specifies the sequences of method invocations that are allowed by that component. We have built an compiler that takes the interface grammar for a component as input and generates a stub for that component. The stub thus generated can be used to replace that component during state space exploration, either to moderate state space explosion, or to provide an executable environment for the component under verification. We conducted a case study by writing an interface grammar for the Enterprise JavaBeans (EJB) persistence interface, and using the resulting stub to check EJB clients using the JPF model checker. Our results show that EJB clients can be verified efficiently with JPF using our approach.

Evolutionary testing of classes

Object oriented programming promotes reuse of classes in multiple contexts. Thus, a class is designed and implemented with several usage scenarios in mind, some of which possibly open and generic. Correspondingly, the unit testing of classes cannot make too strict assumptions on the actual method invocation sequences, since these vary from application to application.In this paper, a genetic algorithm is exploited to automatically produce test cases for the unit testing of classes in a generic usage scenario. Test cases are described by chromosomes, which include information on which objects to create, which methods to invoke and which values to use as inputs. The proposed algorithm mutates them with the aim of maximizing a given coverage measure. The implementation of the algorithm and its application to classes from the Java standard library are described.

Automated Testing of Classes

Programs developed with object technologies have unique features that often make traditional testing methods inadequate. Consider, for instance, the dependence between the state of an object and the behavior of that object: The outcome of a method executed by an object often depends on the state of the object when the method is invoked. It is therefore crucial that techniques for testing of classes exercise class methods when the method's receiver is in different states. The state of an object at any given time depends on the sequence of messages received by the object up to that time. Thus, methods for testing object-oriented software should identify sequences of method invocations that are likely to uncover potential defects in the code under test. However, testing methods for traditional software do not provide this kind of information. In this paper, we use data flow analysis, symbolic execution, and automated deduction to produce sequences of method invocations exercising a class under test. Since the static analysis techniques that we use are applied to different subproblems, the method proposed in this paper can automatically generate information relevant to testing even when symbolic execution and automated deduction cannot be completed successfully.