How Artificial Intelligence Can Revolutionize Software Testing Techniques

Krichen, Moez

doi:10.1007/978-3-031-27499-2_18

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Overall, the paper sheds light on the importance and potential of P-TaaS in enhancing the efficiency and effectiveness of performance testing in cloud computing environments. [10] In this article, the authors explored the various strategies for software testing and the diverse approaches to artificial intelligence in the testing domain. Highlighted the primary benefits that arise from incorporating artificial intelligence into the software testing process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Structured Cloud-Based Software Testing Model with a Case Study Implementation

Santhosh S

2023

IJRITCC

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Cloud-based testing methodologies were gaining significant popularity and adoption in the software testing industry. Cloud-based testing offers several advantages, such as scalability, flexibility, cost-effectiveness, and the ability to access a wide range of testing tools and environments without the need for extensive infrastructure setup. Cloud testing methods are having challenges with respect to testing priority, practical use cases, performance, lengthy test time, integrating and streamlining, data security, etc. since they are addressing specific purposes. To address these challenges, there is a need for a structured testing model with respect to the cloud environment. This article proposes a new structured cloud-based testing model for enhancing the testing service in the cloud environment. The proposed model addresses the order of testing and the priority, data security, and performance by using Smoke and Sanity testing methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Structured Cloud-Based Software Testing Model with a Case Study Implementation

Santhosh S

2023

IJRITCC

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“…However, unit tests, even when covering code with a high percentage, cannot verify all possible inputs and paths of execution due to the state explosion problem [15]. It is still visible despite using AI methods to improve unit testing [16,17]. This is why failure in later stages of development is inevitable and must be tackled, especially when modifications in several components, delivering new functionality, are ready, and integration tests are performed to ensure the correctness of the delivered implementation on a multi-component level.…”

Section: Software Lifetime and Fault Diagnosismentioning

confidence: 99%

Software Failure Log Analysis for Engineers—Review

2023

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The use of automated methods for log analysis is unavoidable in any large company; therefore, it has attracted attention from engineers and researchers. As a result, the number of articles in the field grows yearly and new approaches are frequently proposed. Unfortunately, published research works only sometimes meet the needs of engineers wishing to apply the methods in real-life systems. A common issue is that the method’s benefits often do not compensate for the effort required for its implementation and maintenance. Therefore, engineers must understand the pros and cons of full-scale applications, including the implementation details and the required effort. This work provides a comprehensive review of automated log analysis methods and aims to offer a guide for software engineers who fix integration and production failures. The article categorizes and provides an overview of existing methods and assesses their implementation and maintenance costs, as well as the feasibility of the methods. The article also identifies and describes the shortcomings of existing methods, including concept drift, which is not addressed with sufficient attention, as well as the lack of online benchmarks and the interpretation of the log sequence as a language, without an in-depth analysis of its properties. Despite growing efforts to provide feasible and widely adopted solutions, many reference implementations are unavailable. Consequently, the time and computation complexities differ between various implementations of the same approaches, making the results of research work difficult to replicate in real-life scenarios.

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“…Dans les tests de logiciels, l'intelligence artificielle est un mélange d'apprentissage automatique, d'automatisation cognitive, de raisonnement, d'analyse et de traitement du langage naturel (10). L'automatisation cognitive repose sur plusieurs approches technologiques telles que l'exploration de données, la technologie sémantique, l'analyse de texte, l'apprentissage automatique et le traitement du langage naturel.…”

Section: Introductionunclassified

How Artificial Intelligence Can Revolutionize Software Testing Techniques

Krichen

2023

Lecture Notes in Networks and Systems

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Depuis la fin des années 2000, les objets connectés, applications et autres outils numériques innovants foisonnent et ne cessent de se développer. Or, si l'évolution numérique permet de toucher un large public, les bugs peuvent devenir une véritable menace pour la pérennité des grandes entreprises. Dans cet article, nous ferons un bref tour d'horizon des nombreuses stratégies de test des logiciels ainsi que des différentes approches de l'intelligence artificielle. En outre, nous fournissons un aperçu des principaux avantages qui découlent de l'utilisation de méthodes artificielles au cours du processus de test de logiciels. De plus, nous fournissons quelques exemples d'outils basés sur l'intelligence artificielle qui ont été spécifiquement développés dans le but de tester des logiciels.

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How Artificial Intelligence Can Revolutionize Software Testing Techniques

Cited by 5 publications

References 14 publications

A Structured Cloud-Based Software Testing Model with a Case Study Implementation

A Structured Cloud-Based Software Testing Model with a Case Study Implementation

Software Failure Log Analysis for Engineers—Review

How Artificial Intelligence Can Revolutionize Software Testing Techniques

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