Why do we break APIs? First answers from developers

Xavier, Laerte; Hora, André; Valente, M.

doi:10.1109/saner.2017.7884640

Cited by 15 publications

(21 citation statements)

References 12 publications

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“…Additional studies have focused specifically on how developers manage dependencies and versions within environment configuration. Xavier, Hora, and Valente note that developers deliberately make breaking changes to APIs for several reasons related to code maintenance [23]. Further, developers struggle with different options for specifying dependency versions that are supported by many package management systems [24], [25], and analysis of such systems motivates the need for better tooling to deal with problems related to versions [26].…”

Section: Related Workmentioning

confidence: 99%

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over time. Recent work has identified the problem of validating and detecting out-of-date code snippets as the most important consideration for code reuse. However, determining if a snippet is correct, but simply out-of-date, is a non-trivial task. In the best case, breaking changes are well documented, allowing developers to manually determine when a code snippet contains an out-of-date API usage. In the worst case, determining if and when a breaking change was made requires an exhaustive search through previous dependency versions.We present V2, a strategy for determining if a code snippet is out-of-date by detecting discrete instances of configuration drift, where the snippet uses an API which has since undergone a breaking change. Each instance of configuration drift is classified by a failure encountered during validation and a configuration patch, consisting of dependency version changes, which fixes the underlying fault. V2 uses feedback-directed search to explore the possible configuration space for a code snippet, reducing the number of potential environment configurations that need to be validated. When run on a corpus of public Python snippets from prior research, V2 identifies 248 instances of configuration drift.

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Section: Related Workmentioning

confidence: 99%

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…There were also changes not yet documented like the removal of ProgramStageDataElements endpoint. It has completely disappeared in version 2.27, and this was documented nowhere 5 . From the other hand, we saw that changes that appeared in documentation were explained in details.…”

Section: B Dhis2 Use Casementioning

confidence: 96%

“…However, the latter also face challenges and difficulties in managing and evolving API. Xavier et al [5] made a survey to reveal the reasons why producers break APIs. Brito et al [4] did a reasonbased classification of changes in APIs of 400 libraries.…”

Section: A Api Evolutionmentioning

confidence: 99%

“…When performing evolutive actions, API producers are driven by different reasons, e.g., to add new features, to fix bugs, to simplify the API, to improve maintainability, or to improve the security of the API [3]- [5]. Their identification completes the big picture of the evolution of API.…”

Section: Classification Of Changesmentioning

confidence: 99%

“…. In previous studies [4], [5], interviews were conducted to API producers, in order to get their intention for every change. Even though this is the most straightforward way, this information can be found also in API artifacts, where API providers provide hints about the causes of the changes they perform.…”

Section: Rq3mentioning

confidence: 99%

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Classification of Changes in API Evolution

Koçi

Franch

Jovanovic

et al. 2019

2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)

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Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs). Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle. These changes are of different types, caused by different needs and are affecting consumers in different ways. In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs. The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution. Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them. In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).

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DepMiner: Automatic Recommendation of Transformation Rules for Method Deprecation

Zaitsev

Ducasse

Anquetil

et al. 2022

Reuse and Software Quality

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Software applications often depend on external libraries and must be updated when one of those libraries releases a new version. To make this process easier, library developers try to reduce the negative effect of breaking changes by deprecating the API elements before removing them and suggesting replacements to the clients. Modern programming languages and IDEs provide powerful tools for deprecations that can reference the replacement or incorporate the rules written by library developers and use them to automatically update the client code. However, in practice library developers often miss the deprecation opportunities and fail to document the deprecations. In this work, we propose to help library developers support their clients with better deprecations. We rely on the transforming deprecations offered by Pharo and use data mining to detect the missing deprecation opportunities and generate the transformation rules. We implemented our approach for Pharo in a prototype tool called DepMiner. We have applied our tool to five open-source projects and proposed the generated deprecations to core developers of those projects. 63 recommended deprecations were accepted as pull requests.

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Why do we break APIs? First answers from developers

Cited by 15 publications

References 12 publications

V2: Fast Detection of Configuration Drift in Python

V2: Fast Detection of Configuration Drift in Python

Classification of Changes in API Evolution

DepMiner: Automatic Recommendation of Transformation Rules for Method Deprecation

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