Software Change Proneness Prediction Using Machine Learning

Abbas, Raja Tariq; Albalooshi, Fawzi; Hammad, Mustafa

doi:10.1109/3ict51146.2020.9311978

Cited by 7 publications

(13 citation statements)

References 17 publications

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“…Change-proneness was measured in the literature (e.g., Abbas et al, 2020;Bansal et al, 2022;Catolino & Ferrucci, 2019;Zhu et al, 2022) by taking differences between consecutive releases without considering the significance of the change. Change-proneness has been studied previously without considering the significance of software evolution or change at specific intervals or time windows.…”

Section: Introductionmentioning

confidence: 99%

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

A Shatnawi

2023

IJIKM

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Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

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Section: Introductionmentioning

confidence: 99%

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

A Shatnawi

2023

IJIKM

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“…It has an open REST WAPI, used by more than 60 native applications (consumers). For the analysis, we use logs from two different DHIS2 instances: (1) the World Health Organization (WHO), in their Integrated Data Platform (WIDP), which is used by several WHO departments for routine disease surveillance and country reporting; (2) Médecins Sans Fontières (MSF), used for field data collection and as a central repository for medical data.…”

Section: Use Casesmentioning

confidence: 99%

“…They noted that both history of changes and structural metrics are needed for an accurate assessment. Abbas et al [1] focused more on the best techniques for change-prone prediction using object-oriented metrics (e.g., number of lines of code, weighted methods per class), and concluded that machine-learning methods are beneficial in change prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The bottom part depicts the prediction model creation. We applied machine learning methods, as they have been proven to give accurate results in change-proneness prediction [1]. Moreover, we opted to create a classification model, as we had predefined classes with discrete labels, and we were interested in finding if an endpoint was prone to change or not, more than predicting the exact probability of an endpoint to change (as a regression model would provide).…”

Section: Frameworkmentioning

confidence: 99%

“…To evaluate our framework and the significance of the introduced metrics in prediction, we analyze the District Health Information Software 2 (DHIS2) web API. 1 Usage logs are from February to September 2019, and the logs together contain approximately 2.5 million requests of version 2.30 of the DHIS2 web API. The reason to focus on usage logs from earlier versions of the web API is that we can better observe the impact of the usage on several later releases.…”

Section: Use Casementioning

confidence: 99%

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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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An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems

Khanna,

Bansal

2024

J Software Evolu Process

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Software managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers.

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Software Change Proneness Prediction Using Machine Learning

Cited by 7 publications

References 17 publications

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

A data-driven approach to prescribe web API evolution

An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems

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