A Markov chain model for statistical software testing

Whittaker, James A.; Thomason, Michael G.

doi:10.1109/32.328991

Cited by 341 publications

(186 citation statements)

References 15 publications

Supporting

Mentioning

180

Contrasting

Unclassified

Order By: Relevance

“…When considering the adaptive nature of the mission then estimating the reliability of the system is key for the AUV risk assessment process. Well accepted methods for devising software tests for safety critical systems can be applied to estimate the reliability of adaptive mission systems [36].…”

Section: Discussionmentioning

confidence: 99%

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Brito

Bose

Lewis

et al. 2012

2012 IEEE/OES Autonomous Underwater Vehicles (AUV)

View full text Add to dashboard Cite

Abstract-The Autonomous Underwater Vehicle (AUV) community has for many years recognized the potential benefits made by adapting mission planning on-the-fly. Over the years there has been some degree of success in applying adaptive mission planning to very specific problems. Examples of applications include capabilities for a vehicle to search for, and then modify its trajectory to follow, a feature such as a plume or a thermocline, or to modify its trajectory to avoid an obstacle, or to find and follow a feature such as a pipeline. Despite an evident increase in the number of applications, the use of adaptive mission planning is still in its infancy. There is no doubt that adaptive mission planning will play a pivotal role in future AUV persistent presence. So what is delaying this technology from making the leap towards wider industry acceptance? This paper reviews the literature in adaptive mission planning and uses a failure analysis technique to identify key obstacles for the integration of this technique in wider AUV applications. We use our failure analysis to help devise recommendations for mitigating these obstacles. The complexity of the mathematical approaches used by adaptive techniques is one key obstacle. Perhaps of more importance is that the AUV community is increasingly requiring quantitative assessment of risk associated with the use of AUVs. We propose that probability is the appropriate measure for quantifying the risk of adaptive systems and their uncertainty. The work here presented is a collective endeavor of the Engineering Committee on Oceanic Resources Specialist Panel on Underwater Vehicles.

show abstract

Section: Discussionmentioning

confidence: 99%

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Brito

Bose

Lewis

et al. 2012

2012 IEEE/OES Autonomous Underwater Vehicles (AUV)

View full text Add to dashboard Cite

show abstract

“…The Markov usage model (UM) can describe software usage scenario easily, its definition can be found in [7,8,9]. Researchers have proposed many kinds of methods of structuring usage model, which can be summarized to the following methods [9,10,11]: Musa's Method, based on expert experiences, historical data and complex software model.…”

Section: Usage Models 21 Structuring the Usage Modelmentioning

confidence: 99%

“…Markov model is the most widely used model, and the traditional test method is to generate a series of operation sequences through the Markov model [7]. The construction of software usage model should reflect user's real use as far as possible.…”

Section: Introductionmentioning

confidence: 99%

Large scale software test data generation based on collective constraint and weighted combination method

2017

View full text Add to dashboard Cite

Original scientific paper Software reliability test is to test software with the purpose of verifying whether the software achieves reliability requirements and evaluating software reliability level. Statistical-based software reliability testing generally includes three parts: building usage model, test data generation and testing. The construction of software usage model should reflect user's real use as far as possible. A huge number of test cases are required to satisfy the probability distribution of the actual usage situation; otherwise, the reliability test will lose its original meaning. In this paper, we first propose a new method of structuring software usage model based on modules and constraint-based heuristic method. Then we propose a method for the testing data generation in consideration of the combination and weight of the input data, which reduces a large number of possible combinations of input variables to a few representative ones and improves the practicability of the testing method. To verify the effectiveness of the method proposed in this paper, four groups of experiments are organized. The goodness of fit index (GFI) shows that the proposed method is closer to the actual software use; we also found that the method proposed in this paper has a better coverage by using Java Pathfinder to analyse the four sets of internal code coverage.Keywords: constraint; data generation; GFI; software reliability testing; usage model; weighted combination Generiranje ispitnih podataka za softver zasnovano na kolektivnom ograničenju i ponderiranoj metodi kombinacijeIzvorni znanstveni članak Ispitivanje pouzdanosti softvera znači ispitivanje softvera kako bi se provjerilo da li udovoljava zahtjevima pouzdanosti i kako bi se procijenio njegov stupanj pouzdanosti. Statistički temeljeno ispitivanje pouzdanosti softvera općenito uključuje tri dijela: izgradnju modela, generiranje ispitnih podataka i ispitivanje. Stvaranje modela upotrebe softvera treba što je više moguće odražavati korisnikovu stvarnu primjenu. Potreban je ogroman broj ispitivanih slučajeva da bi se zadovoljila distribucija vjerojatnoće u slučaju stvarne upotrebe; inače će ispitivanje pouzdanosti izgubiti originalno značenje. U ovom radu najprije predlažemo novu metodu strukturiranja modela primjene softvera zasnovanu na modulima i heurističkoj metodi koja se temelji na ograničenjima. Zatim predlažemo metodu za generiranje podataka za ispitivanje uzimajući u obzir kombinaciju i težinu ulaznih podataka što smanjuje veliki broj mogućih kombinacija ulaznih varijabli na samo nekoliko reprezentativnih i povećava praktičnost primjene ispitne metode. U svrhu provjere učinkovitosti metode predložene u ovom radu, organizirane su četiri grupe eksperimenata. Ispravnost odgovarajućeg indeksa (GFI-goodness of fit index) pokazuje da je predložena metoda bliža upotrebi aktualnog softvera; također smo ustanovili da ima bolju pokrivenost kod uporabe Java Pathfinder-a za analizu četiri niza pokrivenosti internog koda.

show abstract

“…Though, more testing focus should be given to the parts of the code which are executed frequently [2,3,27]; however, there is also a need for severity analysis for better quality of a system. There are some critical codes exist within a system, which are called in case of an emergency.…”

Section: Severity Analysismentioning

confidence: 99%

Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

Ray

Mohapatra

2012

ISRN Software Engineering

View full text Add to dashboard Cite

Improving the efficiency of a testing process is a challenging task. Prior work has shown that often, a small number of bugs account for the majority of the reported software failures; and often, most bugs are found in a small portion of the source code of a program. First, prioritizing the code elements according to their criticality and then conducting testing, will promote to reveal the important bugs at the early phase of testing. Keeping it in view, we propose an efficient test effort prioritization method that give a chance to the tester to focus more on the parts of the source code that are highly influenced towards the system failures or in which, the failures have high impact on the system. We consider five important factors such as influence towards system failures, average execution time, structural complexity, severity, and business value associated with a component and estimates the criticality of the component within a system. We have experimentally proved that our proposed test effort prioritization approach is effective in revealing important bugs at the early phase of testing as it is linked to external measure of defect severity and business value, internal measure of frequency, complexity, and coupling.

show abstract

A Markov chain model for statistical software testing

Cited by 341 publications

References 15 publications

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Large scale software test data generation based on collective constraint and weighted combination method

Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

Contact Info

Product

Resources

About