Mining Multi-level API Usage Patterns

Saied, Mohamed Aymen; Benomar, Omar; Abdeen, Hani; Sahraoui, Houari

doi:10.1109/saner.2015.7081812

Cited by 39 publications

(28 citation statements)

References 23 publications

(38 reference statements)

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“…More recently, tools such as UP-Miner (Wang et al 2013) have been developed to mine high coverage usage patterns from open source repositories by using multiple clustering steps. In 2015, Saied et alproposed a tool called MLUP to improve upon the clustering of MAPO (Saied et al 2015). This tool also uses the Eclipse JDT compiler to extract API usage from client code; the largest improvement over MAPO is the usage of DBSCAN (Ester et al 1996) instead of the frequent itemset analysis approach to cluster usage patterns.…”

Section: Comparison To Existing Techniquesmentioning

confidence: 99%

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Sawant

Bacchelli

2016

Empir Software Eng

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An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.

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Section: Comparison To Existing Techniquesmentioning

confidence: 99%

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Sawant

Bacchelli

2016

Empir Software Eng

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“…Another example is that JADET and TIKANGA learn that methods such as List.add() and Map.put() are usually invoked in loops and report five missing iterations for respective invocations outside a loop, which are perfectly fine according to the API. Approaches such as multi-level patterns [49] or ALATTIN's alternative patterns [18] may help to mitigate this problem. Also note that the four detectors in our experiments all use absolute frequency thresholds, while some of the detectors from our survey 2.…”

Section: False Positivesmentioning

confidence: 99%

A Systematic Evaluation of Static API-Misuse Detectors

Amann

Nguyen

Nadi

et al. 2019

IIEEE Trans. Software Eng.

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Application Programming Interfaces (APIs) often have usage constraints, such as restrictions on call order or call conditions. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities. Though researchers developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. Therefore, we need to understand the capabilities and limitations of existing detectors in order to advance the state of the art. In this paper, we present the first-ever qualitative and quantitative evaluation that compares static API-misuse detectors along the same dimensions, and with original author validation. To accomplish this, we develop MUC, a classification of API misuses, and MUBENCHPIPE, an automated benchmark for detector comparison, on top of our misuse dataset, MUBENCH. Our results show that the capabilities of existing detectors vary greatly and that existing detectors, though capable of detecting misuses, suffer from extremely low precision and recall. A systematic root-cause analysis reveals that, most importantly, detectors need to go beyond the naive assumption that a deviation from the most-frequent usage corresponds to a misuse and need to obtain additional usage examples to train their models. We present possible directions towards more-powerful API-misuse detectors.

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“…Dekel and Herbsleb improve API documentation by highlighting specific directives that are present in the documentation so that the consumer is made explicitly aware of particular conditions that he has to be aware of [52]. Researchers have investigated ways to improve existing documentation by augmenting it with examples mined, for instance, from source code repositories [44], [53], [54], [55] and StackOverflow [45].…”

Section: Studies On Api Documentation Needs Robillard and Delinementioning

confidence: 99%