Lessons from the JML Project

Leavens, Gary T.; Clifton, Curtis

doi:10.1007/978-3-540-69149-5_15

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…1,2 Iowa State University, 3 University of Central Florida, 4 Bowling Green State University 1,2 Ames,Iowa, 3 Orlando, Florida 4 Bowling Green, Ohio Email: 1 hridesh@iastate.edu, 2 tien@iastate.edu, 3 leavens@eecs.ucf.edu, 4 rdyer@bgsu.edu…”

Section: Inferring Behavioral Specifications From Large-scale Repositmentioning

confidence: 99%

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Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence

Rajan¹,

Nguyen²,

Leavens³

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Despite their proven benefits, useful, comprehensible, and efficiently checkable specifications are not widely available. This is primarily because writing useful, non-trivial specifications from scratch is too hard, time consuming, and requires expertise that is not broadly available. Furthermore, the lack of specifications for widely-used libraries and frameworks, caused by the high cost of writing specifications, tends to have a snowball effect. Core libraries lack specifications, which makes specifying applications that use them expensive. To contain the skyrocketing development and maintenance costs of high assurance systems, this self-perpetuating cycle must be broken. The labor cost of specifying programs can be significantly decreased via advances in specification inference and synthesis, and this has been attempted several times, but with limited success. We believe that practical specification inference and synthesis is an idea whose time has come. Fundamental breakthroughs in this area can be achieved by leveraging the collective intelligence available in software artifacts from millions of open source projects. Finegrained access to such data sets has been unprecedented, but is now easily available. We identify research directions and report our preliminary results on advances in specification inference that can be had by using such data sets to infer specifications.

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Section: Inferring Behavioral Specifications From Large-scale Repositmentioning

confidence: 99%

“…Currently the effort needed for humans to formally specify code is similar to that required to write the code itself [2]. Although the process of writing specifications can help clarify a project's requirements, which can help reduce costs [1], the extra effort stops many developers.…”

Section: A the Problemmentioning

confidence: 99%

Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence

Rajan¹,

Nguyen²,

Leavens³

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

Self Cite

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“…Ownership can also be encoded in the JML specification language which is being used in a number of verification systems, as discussed in the VSTTE paper of Leavens and Clifton [27]. There is impressive agreement about syntax but the semantics is neither formalized nor entirely consistent between projects.…”

Section: Heap Encapsulation Using Auxiliary Statementioning

confidence: 99%

Modular Reasoning in Object-Oriented Programming

Naumann

2008

Verified Software: Theories, Tools, Experiments

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Abstract. Difficulties in reasoning about functional correctness and relational properties of object-oriented programs are reviewed. An approach using auxiliary state is briefly described, with emphasis on the author's work. Some near term challenges are sketched.Formal verification depends on scientific theories of programming, which answer questions such as these: What are good models of computational behavior? What behavioral properties of components are needed for modular reasoning about a composed system? How can such properties be specified and a component be verified, or even derived from its specification? How can a program and justification of its correctness be revised in accord with small revision of its specification? Such questions have well developed answers that are adequate for small programs under strong simplifying assumptions. But many useful programs are quite large and built from complicated components that violate simplifying assumptions.The longstanding challenge of compositional reasoning remains substantially unsolved. Object-oriented programs pose several challenges that are the focus of my recent research, in which auxiliary state is being used to specify encapsulation boundaries and disciplined interdependence. Section 2, explains the approach, accomplishments, and challenges in terms of invariants for shared mutable objects. Section 3 addresses relational properties including data refinement and secure information flow. This line of research has been carried out for Java-like programming languages; I argue in Section 1 for the importance of such languages. Some additional challenges pertinent to objectoriented programming, but not tied to the main theme, are discussed in Section 4. A detailed tutorial on the state-based approach to encapsulation advocated here appears elsewhere [33].Several near-term challenges (1-5 years) are presented here in the setting of sequential object-oriented programs. Because the approach taken here is based on the use of assertions, it is also quite relevant to verification of concurrent object-oriented programs and low level imperative code.

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“…Behavioral specifications (pre-and post-conditions) of application programming interfaces (APIs) could help developers effectively utilize the APIs [17]. However, currently the efforts needed to write behavioral specifications can be quite high [19,32]. To reduce the costs of producing behavioral specifications of commonly used APIs, prior work has utilized large scale code corpus to infer preconditions [27].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

“…Therefore, our algorithm derives the implicit belief that i ≥ 0 (Algorithm 4 lines 4-6). In this case, the belief should not be propagated to any path that is outside the scope of the loop (Algorithm 4 lines[18][19]. Another factor to invalidate the implicit belief is the re-assignment of the loop variant.…”

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confidence: 99%

Exploiting implicit belief to resolve sparse usage problem in usage-based specification mining

Khairunnesa

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Lessons from the JML Project

Cited by 8 publications

References 32 publications

Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence

Inferring Behavioral Specifications from Large-scale Repositories by Leveraging Collective Intelligence

Modular Reasoning in Object-Oriented Programming

Exploiting implicit belief to resolve sparse usage problem in usage-based specification mining

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