“Won’t We Fix this Issue?” Qualitative characterization and automated identification of wontfix issues on GitHub

Panichella, Sebastiano; Sorbo, Andrea Di

doi:10.1016/j.infsof.2021.106665

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…The experimental results show that this method can determine whether the issue is a wontfix issue when submitted. The similarities between these studies and our study are that they employ the title and description of the issue on GitHub to build a JIT issue prediction model, but the difference is that our study is to predict CPC issues, while other studies 75–78 mainly predict other types of issues or assign labels to issues.…”

Section: Related Workmentioning

confidence: 99%

“…There are also some research works [75][76][77][78] to build models to predict different types of issues on GitHub. Herbold et al 75 proposed a method to predict whether an issue on GitHub is a bug issue by considering the difference between the issue title and description.…”

Section: Jit Bug Predictionmentioning

confidence: 99%

“…The experimental results show that the recommendation results of this system are highly consistent with the developers' opinions. Panichella et al 78 first quantitatively analyzed the wontfix issues on GitHub and built a model to predict the wontfix issues based on the characteristics of these issues. The experimental results show that this method can determine whether the issue is a wontfix issue when submitted.…”

Section: Related Workmentioning

confidence: 99%

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Just‐in‐time identification for cross‐project correlated issues

Ren,

Li,

Chen

et al. 2023

J Software Evolu Process

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Issue tracking systems are now prevalent in software development, which would help developers submit and discuss issues to solve development problems on software projects. Most previous studies have been conducted to analyze issue relations within projects, such as recommending similar or duplicate bug issues. However, along with the popularization of co‐developing through multiple projects, many issues are cross‐project correlated (CPC), that is, one issue is associated with another issue in a different project. When developers meet with CPC issues, it may primarily increase the difficulties of solving them because they need information from not only their projects but also other related projects that developers are not familiar with. Identifying a CPC issue as early as possible is a fundamental challenge for both managers and developers to allocate the resources for software maintenance and estimate the effort to solve it. This paper proposes 11 issue metrics of two groups to describe textual summary and reporters' activity, which can be extracted just after the issue was reported. We employ these 11 issue metrics to construct just‐in‐time (JIT) prediction models to identify CPC issues. To evaluate the effect of CPC issue prediction models, we conduct experiments on 16 open‐source data science and deep learning projects and compare our prediction model with two baseline models based on textual features (i.e., Term Frequency‐Inverse Document Frequency [TF‐IDF] and Word Embedding), which are commonly adopted by previous studies on issue prediction. The results show that the JIT prediction model based on issue metrics has significantly improved the performance of CPC issue prediction under two evaluation indicators, Matthew's correlation coefficient (MCC) and F1. In addition, we find that the prediction model is more suitable for large‐scale complex core projects in the open‐source ecosystem.

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Section: Related Workmentioning

confidence: 99%

Section: Jit Bug Predictionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Just‐in‐time identification for cross‐project correlated issues

Ren,

Li,

Chen

et al. 2023

J Software Evolu Process

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“…Driven by our observation that several issues in JIRA were resolved in later versions after their reporting, and supported by existing literature research [68], we examined the later versions of the DHIS2 web API, from version 2.32 to 2.34. This al-lows us to also better understand and interpret the false positive predictions (i.e., endpoints that were predicted changed but remained unchanged).…”

Section: Discussionmentioning

confidence: 98%

“…Gathering, processing and effectively using this feedback, which originates from different sources, can be challenging. Providers have confirmed that they spend a significant time handling consumer requests and their bug reports [68]. Frequently, these requests are duplicated, lack relevance, contain errors, or are misclassified.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach to prescribe web API evolution

Koçi

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(English) In the last two decades, the use of web Application Programming Interfaces (APIs) has grown exponentially, providing both consumers (software developers) and providers (companies and institutions that expose their organizational data or services) with numerous advantages and facilities. Web APIs allow developers to easily integrate existing services or data into their applications, reducing the time and cost required to build new software. At the same time, by making available their web APIs, providers can increase their customer reach or create a new revenue stream by monetizing the web API. Even though consumers expect web APIs to be steady and well-established, web APIs are prone to continuous changes, evolving several times through their lifecycle. As the use of web APIs continues to grow, the need to evolve them to meet the changing needs of consumers becomes more challenging. However, consumers do not always welcome web API evolution, as the changes might be too frequent or not relevant from their point of view. This discontentment becomes even stronger for the fact that web APIs are exposed over the Internet, meaning that consumers may be forced to upgrade to new web API versions (if providers decide to discontinue the former ones). Knowing the impact changes have on consumers, providers have to strike a balance between not imposing unexpected, frequent changes and providing an up-to-date, maintainable, bug-free web API, that fulfills consumers’ needs. The aim of this research work is to develop a semi-automatic method that enables web API providers to prescribe changes based on their consumers' behavior, as recorded in web API usage logs. We take the position that web API evolution should be mainly usage-based, i.e., the way consumers use web APIs should be one of the main drivers of web API changes. We start with the exploration of web API evolution, by seeing the process from the points of view of both providers and consumers. We identify and classify the changes that often happen to web APIs, and investigate how all these changes are reflected in various artifacts. Building on this classification, we examine different types of usage logs, namely development and production logs, to understand consumers' needs based on their behavior. We detect usage patterns that indicate the presence of usability issues or needs for improvement in the web API and suggest changes that should be implemented in future web API releases. Our approach enables providers to make informed decisions based on usage patterns. By adopting a usage-based approach, providers can ensure that their web APIs continue to meet the evolving needs of their consumers. (Català) En les últimes dues dècades, l'ús d'Aplicacions de Programació d'Interfícies (APIs) web ha crescut exponencialment, oferint nombroses avantatges i facilitats tant als consumidors (desenvolupadors de programari) com als proveïdors (empreses i institucions que exposen les seves dades o serveis organitzatius). Les APIs web permeten als desenvolupadors integrar fàcilment serveis o dades existents en les seves aplicacions, reduint el temps i el cost necessaris per construir nous programaris. Al mateix temps, al posar a disposició la seva API web, els proveïdors poden augmentar el nombre de clients de la seva marca o crear una nova font de guanys monetitzant la seva API web. Tot i que els consumidors esperen que les APIs web siguin estables i ben establertes, les APIs web són propenses a canvis contínues, evolucionant diverses vegades al llarg del seu cicle de vida. A mesura que l'ús de les APIs web continua creixent, la necessitat de fer-les evolucionar per satisfer les necessitats canviant dels consumidors esdevé més desafiadora. No obstant això, els consumidors no sempre acullen amb satisfacció l'evolució de les APIs web, ja que els canvis poden ser massa freqüents o no rellevants des del seu punt de vista. Aquesta descontentament esdevé encara més fort pel fet que les APIs web estan exposades a través d'Internet, el que significa que els consumidors poden ser obligats a actualitzar-se a noves versions de la API web (si els proveïdors decideixen discontinuar les antigues). Coneixent l'impacte que els canvis tenen en els consumidors, els proveïdors han de trobar un equilibri entre no imposar canvis inesperats i freqüents i proporcionar una API web actualitzada, mantenible i sense errors, que satisfaci les necessitats dels consumidors. L'objectiu d'aquest treball de recerca és desenvolupar un mètode semi-automàtic que permeti als proveïdors de web API prescriure canvis basats en el comportament dels seus consumidors, tal com es registra en els logs d'ús de la web API. Considerem que l'evolució de la web API ha de ser principalment basada en l'ús, és a dir, la manera com els consumidors utilitzen les web APIs ha de ser un dels principals impulsors dels canvis en la web API. Comencem amb l'exploració de l'evolució de la web API, veient el procés des dels punts de vista tant dels proveïdors com dels consumidors de la API. Identifiquem i classifiquem els canvis que sovint es produeixen en les web APIs, i investiguem com aquests canvis es reflecteixen en diversos artefactes. Analitzem diferents tipus de logs d'ús, concretament els logs de desenvolupament i producció, per entendre les necessitats dels consumidors basades en el seu comportament. Identificant les necessitats dels consumidors, podem prescriure canvis que s'haurien d'implementar en futures versions de la web API. El nostre enfocament permet als proveïdors prendre decisions informades basades en els patrons d'ús. Adoptant un enfocament basat en l'ús, els proveïdors poden garantir que les seves web APIs continuïn satisfent les necessitats en evolució dels seus consumidors.

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