Learning to recommend third-party library migration opportunities at the API level

Alrubaye, Hussein; Mkaouer, Mohamed Wiem; Khokhlov, Igor; Reznik, Leon; Ouni, Ali; Mcgoff, Jason

doi:10.1016/j.asoc.2020.106140

Cited by 29 publications

(12 citation statements)

References 29 publications

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“…Pandita et al (Pandita et al 2015) approached the problem of library migration by analysing textual similarity of documentation from different libraries. Alrubaye et al (Alrubaye et al 2020) proposed a novel machine learning approach that inferred the mapping between the API elements of two different library by extracting various features from library documentation and solving a classification problem. Meng et al (N. Meng et al 2013) help developers apply systematic edits (similar, but not identical, changes to many locations in source code) by learning from examples to find the correct location and apply the correct change.…”

Section: Related Workmentioning

confidence: 99%

Deprewriter: On the fly rewriting method deprecations.

Ducasse¹,

Polito²,

Zaitsev³

et al. 2022

JOT

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Deprecations are a common way to indicate that a given feature or API will not be available in subsequent versions of a library or framework. While raising deprecation warnings lets developers of libraries evolve their APIs, the developers of client applications often have to manually rewrite their applications to adapt to the deprecation (removal, new APIs. . . ). Some may use static analysis tools to support the rewriting. However, dynamically-typed languages or the use of reflective features often produce incorrect rewrite candidates. This is a costly activity that can lead to bug introductions.In this article, we present a method deprecation approach and a tool called DEPREWRITER that can automatically rewrite the callers of deprecated methods during program execution. Clients of a deprecated API execute their program and associated tests, and DEPREWRITER dynamically rewrites the source code of methods that called a deprecated API to use the new API.The implementation of DEPREWRITER is based on dynamic program transformation: when a deprecated method is executed, a program transformation engine rewrites and recompiles the caller's code before continuing the execution.The approach presented in this article has been developed by the Pharo consortium. Since 2016, DEPREWRITER is used in production in multiple distributions of the Pharo programming language: Pharo 6, 7, 8, and 9 alpha. This article presents and validates this approach.The validation is done in two steps: first with an analysis of deprecations available in Pharo 8 and second with an open survey of software developers about DEPREWRITER. We studied 367 Pharo 8 deprecations, among which we analyzed the 218 rewriting deprecations that use transformation rules. We identified the validity conditions and reported defects to the community. We also proposed 33 transformation rules to be added to the non-rewriting deprecations. Both contributions were accepted into Pharo 9 alpha. We classified the rules and identified possible points of improvement. In addition, we performed a user survey and collected information from 46 software developers: some of them used existing DEPREWRITER' rules and executed them on their code, others used DEPREWRITER to create rewriting deprecations, and finally, some were not aware of DEPREWRITER. 28 of 46 developers (60%) reported that the rewriting deprecations helped them, while 10 stated the inverse and 8 were uncertain.After discussing the current implementation, we sketch possible implementations for other languages than Pharo, showing that the approach is general enough to be applied to other languages.

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Section: Related Workmentioning

confidence: 99%

Deprewriter: On the fly rewriting method deprecations.

Ducasse¹,

Polito²,

Zaitsev³

et al. 2022

JOT

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show abstract

“…RAPIM [2] employs a tailored machine learning model to identify and recommend API mappings learned from previously migration changes. Given two TPLs as input, RAPIM extracts valuable method descriptions from their documentation using text engineering techniques and encode them in feature vectors to enable the underpinning machine learning model.…”

Section: Existing Techniquesmentioning

confidence: 99%

“…-Gathering and storing of migration data: Using Neo4j Java Driver, 2 EvoPlan stores the extracted data in a persistent and flexible data structure; -Recommendation of an upgrade plan list: Considering the number of clients, EvoPlan suggests the most common upgrade plans that are compliant with those that have been accepted by the developers community at large; -Modularity and flexible architecture: The proposed system can be seen as both an external module integrable into other approaches and a completely stand-alone tool that can be customized by end users; -Automated evaluation and replication package availability: The performance of EvoPlan has been evaluated by employing the widely used ten-fold crossvalidation technique. Last but not least, we make the EvoPlan replication package available online to facilitate future research.…”

Section: Introductionmentioning

confidence: 99%

Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

Rubei¹,

Ruscio²,

Sipio³

et al. 2022

Preprint

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During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two

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“…Other techniques use various metrics, based on functional and non-functional characteristics of libraries, to select or recommend such analogous libraries [14,19,24]. Similar approaches have also been used for API mapping, whether through mining previous migration instances or API descriptions to discover analogous APIs [4,6,9,37,42,52], or by building recommender systems around these API mappings [5]. For client code transformation, researchers used various techniques such as program synthesis [37], data flow analysis [32], patch generalization [20,51] and differential testing [20].…”

Section: Introductionmentioning

confidence: 99%

“…While the previous work above shows that library migration is an active research area with lots of advancements, there are still gaps in the literature. First, most of the mentioned previous library migration techniques focus on the Java language [5,6,19,20,24,32,[41][42][43]51]. It is not clear whether these techniques can be applied to a different programming language that is not statically typed, such as Python [17,18].…”

Section: Introductionmentioning

confidence: 99%

PyMigBench and PyMigTax: A Benchmark and Taxonomy for Python Library Migration

Islam¹,

Jha²,

Nadi³

2022

Preprint

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Developers heavily rely on Application Programming Interfaces (APIs) from libraries to build their projects. However, libraries might become obsolete, or new libraries with better APIs might become available. In such cases, developers need to replace the used libraries with alternative libraries, a process referred to as library migration. When done manually, library migration can be tedious, time-consuming, and error-prone. Most of the current research on automated library migration techniques focus on Java libraries, and even more so on version migrations of the same library. Despite the increasing popularity of Python, limited research work has investigated migration between Python libraries. In this paper, we investigate the nature of Python library migrations in open-source systems. We analyze the code changes that happen during library migration and build PyMigBench, a manually verified migration benchmark. PyMigBench contains 436 migration-related code changes from 74 commits in 57 client repositories, and includes migrations between 34 unique pairs of libraries. Additionally, we manually analyze the migration-related code changes and create a taxonomy of migrations, PyMigTax, that categorizes migrations across various dimensions. Our contributions provide the necessary foundations for developing automated Python library migration tools and techniques.

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Learning to recommend third-party library migration opportunities at the API level

Cited by 29 publications

References 29 publications

Deprewriter: On the fly rewriting method deprecations.

Deprewriter: On the fly rewriting method deprecations.

Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

PyMigBench and PyMigTax: A Benchmark and Taxonomy for Python Library Migration

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