Abstract

Context:

Software Testing is a costly activity since the size of the test case set tends to increase as the construction of the software evolves. Test Case Prioritization (TCP) can reduce the effort and cost of software testing. TCP is an activity where a subset of the existing test cases is selected in order to maximize the possibility of finding defects. On the other hand, Markov Chains representing a reactive system, when solved, can present the occupation time of each of their states. The idea is to use such information and associate priority to those test cases that consist of states with the highest probabilities.

Objective:

     The objective of this paper is to conduct a survey to identify and understand key initiatives for using Markov Chains in TCP. Aspects such as approaches, developed techniques, programming languages, analytical and simulation results, and validation tests are investigated.

Methods:

     A Systematic Literature Review (SLR) was conducted considering studies published up to July 2021 from five different databases to answer the three research questions.

Results:

     From SLR, we identified 480 studies addressing Markov Chains in TCP that have been reviewed in order to extract relevant information on a set of research questions.

Conclusion:

     The final 12 studies analyzed use Markov Chains at some stage of test case prioritization in a distinct way, that is, we found that there is no strong relationship between any of the studies, not only on how the technique was used but also in the context of the application. Concerning the fields of application of this subject, 6 forms of approach were found: Controlled Markov Chain, Usage Model, Model-Based Test, Regression Test, Statistical Test, and Random Test. This demonstrates the versatility and robustness of the tool. A large part of the studies developed some prioritization tool, being its validation done in some cases analytically and in others numerically, such as: Measure of the software specification, Optimal Test Transition Probabilities, Adaptive Software Testing, Automatic Prioritization, Ant Colony Optimization, Model Driven approach, and Monte Carlo Random Testing.

Introduction

     It is a fact, nowadays, that the question of software reliability is extremely relevant, given the dependence society has on software systems. Quality of all such produced software systems must be ensured, and hence Verification & Validation (V&V) activities play a major role to be employed to achieve this goal [2]. Moreover, software testing is the most used V&V activity in practice [3], [4]. The goal of any software testing effort is to find defects1 in the software product, in this case very related to defects (faults) in the source code.

     Testing activity ensures the conformity of the software product with the requirements defined by the customer, through a systematic execution. However, in order to entirely achieve this goal, the ideal approach is to exhaustively test the software, which is impractical. In order to cope up with this impracticality, techniques are designed to use only a small subset of the input domain, but with a high probability of revealing the presence of defects, if they exist [3]. Therefore, it is essential to adopt a consolidated methodology that makes the test activity viable and effective. Some approaches have already been proposed [5], such as Test Case Selection (TCS), which selects a subset of the test cases for execution according to a specific objective; Test Suite Reduction (TSR), which reduces the size of the test suite; and Test Case Prioritization (TCP), which prioritizes test cases to be executed. The differential of TCP concerning TSR and TCS is that TCP uses the entire test suite. Every single test case will be considered in prioritization, thus reducing the risk of omitting some type of defect. As the main interest of this study is TCP, the paper will focus on its approaches.

Conclusions

     In this paper, we have reported the results of an SLR on TCP using Markov Chains. From the defined search string, a total of 468 studies obtained from five databases were returned (IEEEXplore, ACM, ScienceDirect, SpringerLink, and Scopus), of which only 12 persisted until the last selection step. For this study, 3 research questions were defined, whose objective is not only to clarify the technologies and methodologies involved in the application of Markov Chains and the prioritization of test cases but also to understand how research related to this field is being distributed and studied by the around the world.

     The main objective of this work was to investigate all the approaches and applications related to this topic, so that, based on the correlations between the studies found, we could trace possible future directions regarding this topic. Our motivation for this was the fact that Test Case Prioritization is a profitable field from the point of view of the entire software development cycle, as its application can be useful in many ways, such as saving time and budget since the goal is to optimize the process of software testing, which often consumes a good deal of effort on the part of the development team.

I- Introduction

II- Terminologies

III- Myths in Software Testing

IV- Types of Testing

V- Software Mutation

VI- Related Works

VII- Oncological Mutation

VIII- Proposed Framework

IX- Conclusion And Future Works

References

Abstract

Biologically inspired computing techniques are highly on demand due to the inter-connection of various specialized fields and their requirement of adaptable methods in solving software engineering problems. Mutation testing has acquired much importance in this scenario. The concept of mutation testing has originated from the bio immune system. This paper is a novel study on mutation testing that proposes a framework OSWM (oncological software mutation) from oncological tests performed in human pancreatic cancer cells. The framework connects the relationship between cancer cell tests and software tests ensuring mutation tests owe its origin from human cells tested. The study argues an intelligent software with an efficient immune system can therefore aim at testing, diagnosing and healing on its own.

INTRODUCTION

The significance of software testing is attributed in achieving and evaluating the excellence of software specifications as per the standard requirements. The essentiality of testing a software is huge so as to ensure you deliver the right product to the market.
Software tests can be conducted in different patterns. Typically this depends on the set of data you choose to test your software with. That is the data that can hold more probability of finding errors or that can have lesser probability of finding errors. Therefore any software product is expected to have undergone rigorous testing before it reaches the market especially if has to be dealing with human safety [1]. Thus the understanding of true software testing can make a profound difference in the success of a project [2].
The concept of unethical and dangerous laboratory experiments being eliminated using executable biological prediction software [3] has gained a lot of importance in the bio-inspired conceptual algorithms.
A system has an ability to be itself dependable in a persistent way even when there are failures in the system [4]. This concept is many a time matched with the fault tolerance system in a software to prove its resilience when matched with the bio-immune system.

I- Introduction

II- Literature Review

III- Artificial Intelligence & Software Testing

IV- Conclusion

V- Future Work

References

Abstract

Artificial Intelligence (AI) plays an important role in our life and touch base most of our surrounding applications and systems. A huge amounts of data are created every day from many different sources that need to be monitored and analyzed properly and report results and take actions. A more complex software applications have been built, time is becoming a critical factor to release applications that must be fully tested and comply with Business Requirements. AI plays a key role in Software Testing and can get more accurate results and saves time. This paper discuss the Artificial Intelligence key pillars that can be used in Software Testing. It also open a window on how the future will look like in terms of Artificial Intelligence and the Software Testing. The results show that AI can achieve better results in Software Testing and AI-driven testing will lead the new era of the quality assurance (QA) work in the near future. AI Software Testing will reduce time to market and will increase the efficiency of the organization to produce more sophisticated software and will create smarter automated testing.

INTRODUCTION

Artificial intelligence started playing many roles in the applications around us and soon it will be an essential part of our societies and our life. The oxford definition of artificial intelligence is: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [1]. The key pillars of AI are: machine learning, deep learning , natural language processing (NLP), expert system and others. AI covers many areas like: data analysis, prediction, decision making, intelligent systems and many others.
In recent years, machine learning, deep learning, NLP and the related algorithms and techniques have achieved a great breakthroughs in many fields and specifically in robots industry. Machines started understanding verbal commands, evaluating information, recognizing images, driving cars, analyzing data and playing games better than we do.
Due to the increasing maturity of AI’s algorithms and techniques, and due to the amazing development in the technology and computer hardware that increased the computers speed and provided a huge memory, AI started acting an important role in many areas and one of them is software testing.

مقاله سال 2017

انتظار می رود که ارتباطات دستگاه به دستگاه (D2D), یک ویژگی کلیدی پشتیبانی شده توسط شبکه های نسل پنجم , بخصوص باتوجه به گسترش کامپیوتری نسل پرسرعت تر شبکه موبایل (MEC ), که نقش مهمی در کاهش فشار شبکه با تغییر کارکردهای کامپیوتری از اینترنت به نسل پرسرعت تر شبکه موبایل دارد, باشد. جدا از اینکه قسمتی از MECبوده است , D2D می تواند پوشش سلولی را گسترش دهد و به کاربران امکان ارتباط مستقیم هنگامی که زیرساخت های مخابراتی (ارتباطی دور) بسیار متراکم است یا وجود ندارد, می دهد. این انحراف چشمگیر از پارادایم سلولی معمولی, نیاز به پروتکل های مسیریابی شبکه غیر متمرکز را تحمیل می کند. علاوه بر این، قابلیت های پیشرفته ی دستگاه های تلفن همراه و شبکه های D2D به احتمال زیاد موجب گسترش انواع بدافزارهای جدید و همه گیر می شود. اگر چه مطالعات و تحقیقات, از نظر پروتکل های مسیریابی D2D بسیار غنی بوده ,که موجب افزایش کیفیت سرویس و مصرف انرژی می شود، تنها پشتیبانی امنیتی اساسی، به عنوان مثال، در قالب رمزگذاری را ارائه می کنند. با این وجود, تصمیم مسیریابی می تواند در تشخیص مشترک بدافزارهای تلفن همراه با بهره گیری از انواع مختلف نرم افزار ضد تروجان نصب شده روی دستگاه های تلفن همراه کمک کند. با بهره گیری از ماهیت همیاری ارتباطات D2D، دستگاه می تواند به مشارکت یک دیگر برای تشخیص نرم افزارهای مخرب تکیه کند. تاثیر کار ما روی هدایت شبکه های D2D بدافزارهای رایگان بوده است. برای دسترسی به این موضوع, ما یک پروتکل مسیریابی جدید برای ارتباطات D2D طراحی و راه اندازی کردیم, که تصمیمات مسیریابی برای صراحت بهبود تشخیص بدافزارها را بهینه سازی کردیم. این پروتکل ها مسیرهای شبکه بهینه را از نظر کاهش بدافزار و انرژی صرف شده برای تشخیص نرم افزارهای مخرب، بر اساس یک مدل نظری شناسایی می کنند. قابلیت های متنوع دستگاه های شبکه در حال اجرا در انواع مختلف نرم افزار ضد تروجان و پتانسیل آنها بمنظور بررسی پیام های تقویت شده بسمت یک دستگاه مقصد مورد نظر با استفاده از ابزارهای نظری بازی نفوذ کرده است. تجزیه و تحلیل بهینگی در دو بازی های امنیتی نش و استکلبرگ انجام شده است, که شامل دو حاصل جمع متغیر صفر و غیرصفر و استراتژی های تعادلی است. با انجام شبیه سازی های شبکه، نتایج نظری به دست آمده, تصادفا سناریوهای شبکه ی تولیده شده را نشان می دهد, همچنین نشان می دهد که چگونه پروتکل ما ، از نظر بازده مورد انتظار, پروتکل های مسیریابی معمولی فراتری هستند ، که متشکل از: آسیب امنیتی وارد شده توسط نرم افزارهای مخرب و هزینه تشخیص نرم افزارهای مخرب هستند.