Synthesis of interface specifications for Java classes

AlurRajeev,; ČernýPavol,; MadhusudanP.,; NamWonhong,

doi:10.1145/1047659.1040314

Cited by 113 publications

(97 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…White-box inference based on active automata learning (AAL) has been explored in several works. AAL has been combined with predicate abstraction [ACMN05] to infer interface specifications of Java classes, and with CEGAR [HJM05] to infer interface specifications as finite-state automata without data parameters. In [XSL + 13], AAL is combined with support vector machines to infer constraints on data parameters; in [GRR12], AAL is combined with symbolic execution to recover guards from the analyzed system, producing DFA models where labels are guards over parameters of alphabet symbols.…”

Section: Related Workmentioning

confidence: 99%

Active learning for extended finite state machines

et al. 2016

View full text Add to dashboard Cite

We present a black-box active learning algorithm for inferring extended finite state machines (EFSM)s by dynamic black-box analysis. EFSMs can be used to model both data flow and control behavior of software and hardware components. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing. Our algorithm infers a class of EFSMs called register automata. Register automata have a finite control structure, extended with variables (registers), assignments, and guards. Our algorithm is parameterized on a particular theory, i.e., a set of operations and tests on the data domain that can be used in guards.Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We also show that, under these conditions, our framework induces a generalization of the classical Nerode equivalence and canonical automata construction to the symbolic setting. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

show abstract

Section: Related Workmentioning

confidence: 99%

Active learning for extended finite state machines

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Whaley et al (2002) present static and dynamic analysis techniques for extracting specifications (as finite state machines) for classes, centered around those classes' fields. Alur et al (2005) analyze definitions of classes (as opposed to their clients) to come up with interface specifications for them. Their approach is based on gradual refinement of the specifications using model checking.…”

Section: Related Workmentioning

confidence: 99%

Mining temporal specifications from object usage

WasylkowskiAndrzej

Zeller

2011

Autom Softw Eng

View full text Add to dashboard Cite

A caller must satisfy the callee's precondition-that is, reach a state in which the callee may be called. Preconditions describe the state that needs to be reached, but not how to reach it. We combine static analysis with model checking to mine Fair Computation Tree Logic (CTL F ) formulas that describe the operations a parameter goes through: "In parseProperties(String xml), the parameter xml normally stems from getProperties()." Such operational preconditions can be learned from program code, and the code can be checked for their violations. Applied to ASPECTJ, our TIKANGA prototype found 169 violations of operational preconditions, uncovering 7 unique defects and 27 unique code smells-with 52% true positives in the 25% top-ranked violations.

show abstract

“…Kremenek et al (2006) used Bayesian learning to match methods with a predefined automata template for specifications. Alur et al (2005) used randomly generated test cases as clients and use Angluin's algorithm (Rivest and Schapire 1993) to infer automata from call sequences that do not throw exceptions. Gowri et al (2005) also used a client emulator as clients, and their approach uses some analysis results such as objects' relationships, internal states, and their specifications of libraries.…”

Section: Mining Specificationsmentioning

confidence: 99%

Inferring specifications for resources from natural language API documentation

et al. 2011

View full text Add to dashboard Cite

Many software libraries, especially those commercial ones, provide API documentation in natural languages to describe correct API usages. However, developers may still write code that is inconsistent with API documentation, partially because many developers are reluctant to carefully read API documentation as shown by existing research. As these inconsistencies may indicate defects, researchers have proposed various detection approaches, and these approaches need many known specThis paper is a revised, expanded version of a paper (Zhong et al. 2009b) presented at the 24th IEEE/ACM International Conference on Automated Software Engineering Conference (ASE 2009), which won the best paper award of the conference and the ACM SIGSOFT distinguished paper award. The work of this paper was done when Hao Zhong was a PhD student with Peking University under the supervision of Prof. Hong Mei, and the revisions over the previous ASE 2009 paper (Zhong et al. 2009b) were done when Hao Zhong became an assistant professor with Chinese Academy of Sciences since 2009. 228Autom Softw Eng (2011) 18:227-261 ifications. As it is tedious to write specifications manually for all APIs, various approaches have been proposed to mine specifications automatically. In the literature, most existing mining approaches rely on analyzing client code, so these mining approaches would fail to mine specifications when client code is not sufficient. Instead of analyzing client code, we propose an approach, called Doc2Spec, that infers resource specifications from API documentation in natural languages. We evaluated our approach on the Javadocs of five libraries. The results show that our approach performs well on real scale libraries, and infers various specifications with relatively high precisions, recalls, and F-scores. We further used inferred specifications to detect defects in open source projects. The results show that specifications inferred by Doc2Spec are useful to detect real defects in existing projects.

show abstract

Synthesis of interface specifications for Java classes

Cited by 113 publications

References 40 publications

Active learning for extended finite state machines

Active learning for extended finite state machines

Mining temporal specifications from object usage

Inferring specifications for resources from natural language API documentation

Contact Info

Product

Resources

About