Detecting Missing Method Calls in Object-Oriented Software

Monperrus, Martin; Bruch, Marcel; Mezini, Mira

doi:10.1007/978-3-642-14107-2_2

Cited by 45 publications

(58 citation statements)

References 19 publications

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“…Wasylkowski et al [37], [38] present analyses to statically detect missing method calls. Similarly, Nguyen et al [29] and Monperrus et al [28] learn usage patterns to find code locations where a particular call seems to be missing. All these anomaly detection approaches differ from our work in the kind of anomalies they search.…”

Section: Related Workmentioning

confidence: 99%

Name-Based Analysis of Equally Typed Method Arguments

Pradel

Groß

2013

IIEEE Trans. Software Eng.

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Abstract-When calling a method that requires multiple arguments, programmers must pass the arguments in the expected order. For statically typed languages, the compiler helps programmers by checking that the type of each argument matches the type of the formal parameter. Unfortunately, types are futile for methods with multiple parameters of the same type. How can a programmer check that equally typed arguments are passed in the correct order? This paper presents two simple yet effective, static program analyses that detect problems related to the order of equally typed arguments. The key idea is to leverage identifier names to infer the semantics of arguments and their intended positions. The analyses reveal problems that affect the correctness, understandability, and maintainability of a program, such as accidentally reversed arguments and misleading parameter names. Most parts of the analyses are languageagnostic. We evaluate the approach with 24 real-world programs written in Java and C. Our results show the analyses to be effective and efficient. One analysis reveals anomalies in the order of equally typed arguments; it finds 54 relevant problems with a precision of 82%. The other analysis warns about misleading parameter names and finds 31 naming bugs with a precision of 39%.

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Section: Related Workmentioning

confidence: 99%

Name-Based Analysis of Equally Typed Method Arguments

Pradel

Groß

2013

IIEEE Trans. Software Eng.

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“…Mined specifications can be used for bug finding by searching for anomalies within the extracted specifications [25]- [28] or by feeding the specifications into program checkers [14], [29]. In contrast to our work, these approaches either require input to run a program, produce false positives, or both.…”

Section: A Bug Finding With Mined Specificationsmentioning

confidence: 99%

Leveraging test generation and specification mining for automated bug detection without false positives

Pradel¹

2012

2012 34th International Conference on Software Engineering (ICSE)

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Abstract-Mining specifications and using them for bug detection is a promising way to reveal bugs in programs. Existing approaches suffer from two problems. First, dynamic specification miners require input that drives a program to generate common usage patterns. Second, existing approaches report false positives, that is, spurious warnings that mislead developers and reduce the practicability of the approach. We present a novel technique for dynamically mining and checking specifications without relying on existing input to drive a program and without reporting false positives. Our technique leverages automatically generated tests in two ways: Passing tests drive the program during specification mining, and failing test executions are checked against the mined specifications. The output are warnings that show with concrete test cases how the program violates commonly accepted specifications. Our implementation reports no false positives and 54 true positives in ten well-tested Java programs.

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“…Rules are then checked by instrumenting and executing the client's source code. A third approach that focuses on bug detection is the one of Monperrus et al [14]. They collect statistics about type-usages.…”

Section: Goalmentioning

confidence: 99%

“…Later works include evaluations using cross-validation techniques [13], [14]. All techniques described in this section were evaluated on a small number of systems, so there exist practically no results generalizable between target systems.…”

Section: Empirical Findingsmentioning

confidence: 99%

Automated API Property Inference Techniques

Robillard

Bodden

Kawrykow

et al. 2013

IIEEE Trans. Software Eng.

Self Cite

171

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Abstract-Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.

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Detecting Missing Method Calls in Object-Oriented Software

Cited by 45 publications

References 19 publications

Name-Based Analysis of Equally Typed Method Arguments

Name-Based Analysis of Equally Typed Method Arguments

Leveraging test generation and specification mining for automated bug detection without false positives

Automated API Property Inference Techniques

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