Black Box Testing With No Knowledge of the System
A Comparative Study of White Box, Black Box and Grey Box Testing Techniques
Abstract and Figures
Software testing is the process to uncover requirement, design and coding errors in the program. It is used to identify the correctness, completeness, security and quality of software products against a specification. Software testing is the process used to measure the quality of developed computer software. It exhibits all mistakes, errors and flaws in the developed software. There are many approaches to software testing, but effective testing of complex product is essentially a process of investigation, not merely a matter of creating and following route procedure. It is not possible to find out all the errors in the program. This fundamental problem in testing thus throws an open question, as to what would be the strategy we should adopt for testing. In our paper, we have described and compared the three most prevalent and commonly used software testing techniques for detecting errors, they are: white box testing, black box testing and grey box testing.
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A Comparative Study of White Box, Black Box and
Grey Box Testing Techniques
Mohd. Ehmer Khan
Department of Computer Science,
Singhania University,
Jhunjhunu, Rajasthan, India
Farmeena Khan
Department of Computer Science,
EIILM University,
Jorethang, Sikkim, India
Abstract—Software testing is the process to uncover requirement,
design and coding errors in the program. It is used to identify the
correctness, completeness, security and quality of software
products against a specification. Software testing is the process
used to measure the quality of developed computer software. It
exhibits all mistakes, errors and flaws in the developed software.
There are many approaches to software testing, but effective
testing of complex product is essentially a process of
investigation, not merely a matter of creating and following route
procedure. It is not possible to find out all the errors in the
program. This fundamental problem in testing thus throws an
open question, as to what would be the strategy we should adopt
for testing. In our paper, we have described and compared the
three most prevalent and commonly used software testing
techniques for detecting errors, they are: white box testing, black
box testing and grey box testing.
Keywords- Black Box; Grey Box; White Box.
I. INTRODUCTION
Software testing identifies defects, flaws or errors in the
application code that must be fixed. We can also define
software testing as a process of accessing the functionality and
correctness of a software through analysis. The main purpose
of testing can be quality assurance, reliability estimation,
validation and verification. Software testing is a fundamental
component of software quality assurance and represents a
review of specification, design and coding. The main objective
of software testing is to affirm the quality of software system
by systematically testing the software in carefully controlled
circumstances, another objective is to identify the completeness
and correctness of the software, and finally it uncovers
undiscovered errors. [1] [2]
The three most important techniques that are used for
finding errors are: [1]
1) White Box Testing Technique: It is the detailed
investigation of internal logic and structure of the code. In
white box testing it is necessary for a tester to have full
knowledge of source code.
2) Black Box Testing Technique: It is a technique of
testing without having any knowledge of the internal working
of the application. It only examines the fundamental aspects of
the system and has no or little relevance with the internal
logical structure of the system.
3) Grey Box Testing Technique: White box + Black box =
Grey box, it is a technique to test the application with limited
knowledge of the internal working of an application and also
has the knowledge of fundamental aspects of the system.
II. WHITE BOX TESTING TECHNIQUE
Input Output
Figure 1. Represent white box testing
White box testing is a test case design method that uses the
control structure of the procedural design to derive test cases.
White box testing can uncover implementation errors such as
poor key management by analyzing internal workings and
structure of a piece of software. White box testing is applicable
at integration, unit and system levels of the software testing
process. In white box testing the tester needs to have a look
inside the source code and find out which unit of code is
behaving inappropriately. [3]
Some of the advantages and disadvantages of white box
testing technique are listed below: [3] [4]
Advantages
It reveals error in hidden code by removing extra lines
of code.
Side effects are beneficial.
Analyse
internal
working only
Risk analysis
Develop Strategy
Proper Test Plan
Execute test Cases
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Maximum coverage is attained during test scenario
writing.
Disadvantages
It is very expensive as it requires a skilled tester to
perform it.
Many paths will remain untested as it is very difficult
to look into every nook and corner to find out hidden
errors.
Some of the codes omitted in the code could be missed
out.
Some of the synonyms of white box testing are glass box
testing, clear box testing, open box testing, transparent box
testing, structural testing, logic driven testing and design based
testing.
Some important types of white box testing techniques are
briefly described below: [3]
Control Flow Testing
Branch Testing
White Box Testing Basis Path Testing
Data Flow Testing
Loop Testing
Figure 2. Represent different forms of white box testing techniques
1) Control Flow Testing: It is a structural testing strategy
that uses the program control flow as a model control flow and
favours more but simpler paths over fewer but complicated
path.
2) Branch Testing: Branch testing has the objective to test
every option (true or false) on every control statement which
also includes compound decision.
3) Basis Path Testing: Basis path testing allows the test
case designer to produce a logical complexity measure of
procedural design and then uses this measure as an approach
for outlining a basic set of execution paths.
4) Data Flow Testing: In this type of testing the control
flow graph is annoted with the information about how the
program variables are define and used.
5) Loop Testing: It exclusively focuses on the validity of
loop construct.
III. BLACK BOX TESTING TECHNIQUE
Processing Unit
Input Output
Figure 3. Represent black box testing
Black box testing treats the software as a "Black Box" –
without any knowledge of internal working and it only
examines the fundamental aspects of the system. While
performing black box test, a tester must know the system
architecture and will not have access to the source code. [5]
Some of the advantages and disadvantages of black box
testing technique are listed below: [4] [5]
Advantages
Efficient for large code segment.
Tester perception is very simple.
Users perspective are clearly separated from
developers perspective (programmer and tester are
independent of each other).
Quicker test case development.
Disadvantages
Only a selected number of test scenarios are actually
performed. As a result, there is only limited coverage.
Without clear specification test cases are difficult to
design.
Inefficient testing.
Some of the synonyms of black box testing technique are
opaque testing, functional testing, close box testing, and
behavioural testing.
Some important types of black box testing techniques are
briefly described below: [5]
Equivalence Partitioning
Boundary Value Analysis
Fuzzing
Black Box Testing Cause-Effect Graph
Orthogonal Array Testing
All Pair Testing
State Transition Testing
Figure 4. Represent differnet forms of black box testing techniques
1) Equivalence Partitioning: It can reduce the number of
test cases, as it divides the input data of a software unit into
partition of data from which test cases can be derived.
2) Boundary Value Analysis: It focuses more on testing at
boundaries, or where the extreme boundary values are chosen.
It includes minimum, maximum, just inside/outside
boundaries, error values and typical values.
3) Fuzzing: Fuzz testing is used for finding
implementation bugs, using malformed/semi-malformed data
injection in an automated or semi-automated session.
4) Cause-Effect Graph: It is a testing technique, in which
testing begins by creating a graph and establishing the relation
between the effect and its causes. Identity, negation, logic OR
Analyse
Fundamental
aspects only
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and logic AND are the four basic symbols which expresses the
interdependency between cause and effect.
5) Orthogonal Array Testing: OAT can be applied to
problems in which the input domain is relatively small, but too
large to accommodate exhaustive testing.
6) All Pair Testing: In all pair testing technique, test cases
are designs to execute all possible discrete combinations of
each pair of input parameters. Its main objective is to have a
set of test cases that covers all the pairs.
7) State Transition Testing: This type of testing is useful
for testing state machine and also for navigation of graphical
user interface.
IV. G REY BOX T ESTING T ECHNIQUE
Grey box testing technique will increase the testing
coverage by allowing us to focus on all the layers of any
complex system through the combination of all existing white
box and black box testing.
Processing Unit
Input Output
Figure 5. Represent grey box testing
In grey box testing the tester must have knowledge of
internal data structures and algorithm, for the purpose of
designing test cases. Examples of grey box testing technique
are: [6]
Architectural Model
Unified Modeling language (UML)
State Model (Finite State Machine)
In grey box testing the codes of two modules are studied
(white box testing method) for the design of test cases and
actual test are performed in the interfaces exposed (black box
testing method).
Some of the advantages of grey box testing technique are
listed below: [4] [6]
Grey box testing provides combined benefits of white
box and black box testing techniques.
In grey box testing, the tester relies on interface
definition and functional specification rather than
source code.
In grey box testing, the tester can design excellent test
scenarios.
The test is done from the user's point of view rather
than designer's point of view.
Create an intelligent test authoring.
Unbiased testing.
Some of the disadvantages of grey box testing technique
are listed below:
Test coverage is limited as the access to source code is
not available.
It is difficult to associate defect identification in
distributed applications.
Many program paths remain untested.
If the software designer has already run a test case, the
tests can be redundant.
The other name of grey box testing is translucent testing.
Different forms of grey box testing techniques are briefly
described below: [6]
Orthogonal Array Testing
Matrix Testing
Grey Box Testing Regression Testing
Pattern Testing
Figure 6. Represent different forms of grey box testing techniques
1) Orthogonal Array Testing: This type of testing use as
subset of all possible combinations.
2) Matrix Testing: In matrix testing the status report of the
project is stated.
3) Regression Testing: If new changes are made in
software, regression testing implies running of test cases.
4) Pattern Testing: Pattern testing verifies the good
application for its architecture and design.
V. FUTURE OF S OFTWARE T ESTING
With the changing trends in the software industry, software
testing too changes. The existing new technologies like Service
Oriented Architecture (SOA), wireless technologies, mobile
services etc. has opened new path to testing. Some of the
changes which we will see in the industry over the next few
years are listed below: [8]
Testers will provide light weight models that
developers can run against their codes.
Early review and modeling will exposes many
ambiguous bugs.
As in the future developer's code is full of testability
hooks, errors will be more detectable.
Static analyser (detection tools) will come in main
stream.
Useful matrices such as spec coverage, model coverage
and code coverage drives the projects.
Combinatorial tools will allow testers to prioritize their
testing.
The testers will provide visible and value added
services throughout the software development process.
Tester can develop test harnesses stubs and drivers
written in and interacting with a variety of
programmatic languages.
Partially analyse
internal working
+
Fundamental aspects
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Tomorrows' tester will be professionally more educated, examine and accredited professional.
TABLE I. C OMPARISON B ETWEEN T HREE FORMS OF TESTING TECHNIQUES [6] [7]
Analyses fundamental aspects
only i.e. no proved edge of
internal working
Partial knowledge of internal
working
Full knowledge of internal
working
Performed by end users and also
by tester and developers (user
acceptance testing)
Performed by end users and also
by tester and developers (user
acceptance testing)
It is performed by developers
and testers
Testing is based on external
exceptions – internal behaviour
of the program is ignored
Test design is based on high
level database diagrams, data
flow diagrams, internal states,
knowledge of algorithm and
architecture
It is least exhaustive and time
consuming
It is somewhere in between
Potentially most exhaustive and
time consuming
It can test only by trial and error
method
Data domains and internal
boundaries can be tested and
over flow, if known
Test better: data domains and
internal boundaries
Not suited for algorithm testing
Not suited for algorithm testing
It is suited for algorithm testing
(suited for all)
In the near future we will see a shift towards new
techniques and testing transformed business operations, the
way people interact with the systems and information it
provides, and therefore mitigating the risk and increasing the
benefits of business change.
VI. CONCLUSION
We can define software testing as an activity aimed at
evaluating an attribute, or capability of a program to determine,
that it meets its required specification. Software testing can
provide an independent view of the software to allow the
business to appreciate and understand the risk of software
implementation.
To carry out software testing in a more effective manner, in
our paper we have described and compared three main software
testing techniques.
REFERENCES
[1] Mohd. Ehmer Khan, "Different Forms of Software Testing Techniques
for Finding Errors," IJCSI, Vol. 7, Issue 3, No 1, pp 11-16, May 2010
[2] Software Testing Methodologies by Azhar available at http://azhar-
paperpresentation.blogspot.com/2010/04/software-testing-
methodologies.html
[3] Mohd. Ehmer Khan, "Different Approaches to White Box testing
Technique for Finding Errors," IJSEIA, Vol. 5, No. 3, pp 1-13, July
2011
[4] Software Testing Methods available at
http://www.tutorialspoint.com/software_testing/testing_methods.htm
[5] Mohd. Ehmer Khan, "Different Approaches to Black Box Testing
Technique for Finding Errors," IJSEA, Vol. 2, No. 4, pp 31-40, October
2011
[6] Grey Box Testing from Wikipedia available at
http://en.wikipedia.org/wiki/Gray_box_testing
[7] Comparison among Black Box Testing, Gray Box Testing & White Box
Testing available at
http://www.softwaretestinggenius.com/articalDetails?qry=404
[8] Predicting the Future of Testing by Harry Robinson available at
http://www.stickyminds.com/sitewide.asp?Function=edetail&ObjectType
=COL&ObjectId=6887
AUTHORS PROFILE
Mohd. Ehmer Khan
I completed my B.Sc in 1997 and M.C.A. in 2001 from Aligarh Muslim
University, Aligarh, India, and pursuing Ph.D (Computer Science) from
Singhania University, Jhunjhunu, India. I have worked as a lecturer at Aligarh
College Engineering & Management, Aligarh, India from 1999 to 2003. From
2003 to 2005 worked as a lecturer at Institute of Foreign Trade &
Management, Moradabad, India. From 2006 to present working as a lecturer
in the Department of Information Technology, Al Musanna College of
Technology, Ministry of Manpower, Sultanate of Oman. I am recipient of PG
Merit Scholarship in MCA. My research area is software engineering with
special interest in driving and monitoring program executions to find bugs,
using various software testing techniques.
Farmeena Khan
I completed my BCA in 2003 from Dr. B. R. Ambedkar University, Agra,
India and MBA in 2011 from Janardan Rai Nagar Rajasthan Vidyapeeth
University, Rajasthan, India, and pursuing M.Sc (Computer Science) from
EIILM University, Jorethang, Sikkim, India.
... The structure of the software is examined by executing the code. In this type of testing, a tester must have full knowledge about source code [21]; Grey-Box testing integrate both white box and black-box testing. The program in this technique is tested with poor knowledge of the internal structure as well as a basic understanding of the system [4]. ...
... This is a simple method that only searches the search space by randomly selecting solutions and evaluating their suitability. Although it is a relatively unspecific strategy, it can be implemented with little effort [21]. ...
Software testing is an important phase in software development. Faults can cause serious and costly problems if they are neglected in software development, such as programs used in the fields of medicine, aviation, and military operations. A genetic algorithm (GA) is an evolutionary algorithm that can help to generate test data very quickly and accurately, generating test cases that fit the software under test. in this research, we generate test data for software that contains pointers using GA where these test data are valid for the software regardless of which path to use. The results of the experiments demonstrate that the Genetic Algorithm gives good results once used as test data generators to test pointer data type; such that the test target in all programs under test is reached which means that the percentage of the coverage was (100 %). Also, it shows the effect of using pointers in the source code, where the results were less in terms of execution time and the6 same in terms of the number of generations for a program that does not contain pointers than the same program which contains pointers.
... This essay credits current research programs in OBwhere phenomena and paradigms are viewed and interpreted by offering theory to answer research questionsin the white box. Such a depiction and understanding of a white box is borrowed from computer science and software model testing as a space where there is sufficient conceptual knowledge (Khan & Khan, 2012). The white box model has also been used by scientists in biology (Leifsson, 2008) and cryptography (Bock, 2019). ...
-
![Jestine Philip]()
Purpose Emerging technologies are capable of enhancing organizational- and individual-level outcomes. The organizational behavior (OB) field is beginning to pursue opportunities for researching emerging technologies. This study aims to describe a framework consisting of white, black and grey boxes to demonstrate the tight coupling of phenomena and paradigms in the field and discusses deconstructing OB's white box to encourage data-driven phenomena to coexist in the spatial framework. Design/methodology/approach A scoping literature review was conducted to offer a preliminary assessment of technology-oriented research currently occurring in OB. Findings The literature search revealed two findings. First, the number of published papers on emerging technologies in top management journals has been increasing at a steady pace. Second, various theoretical perspectives at the micro- and macro- organizational level have been used so far for conducting technology-oriented research. Originality/value By conducting a scoping review of emerging technologies research in OB literature, this paper reveals a conceptual black box relating to technology-oriented research. The essay advocates for loosening OB's tightly coupled white box to incorporate emerging technologies both as a phenomenon and as data analytical techniques.
... Lastly, in software fuzz testing, test generation can be classified into white-box, gray-box, and black-box methods, depending on whether the inner workings of the software is known to the tester [32], [33], [34], [35]. Our application is different, in that the planner is reactive, tightly interacting with a physical environment simulator. ...
High-performance autonomy often must operate at the boundaries of safety. When external agents are present in a system, the process of ensuring safety without sacrificing performance becomes extremely difficult. In this paper, we present an approach to stress test such systems based on the rapidly exploring random tree (RRT) algorithm. We propose to find faults in such systems through adversarial agent perturbations, where the behaviors of other agents in an otherwise fixed scenario are modified. This creates a large search space of possibilities, which we explore both randomly and with a focused strategy that runs RRT in a bounded projection of the observable states that we call the objective space. The approach is applied to generate tests for evaluating overtaking logic and path planning algorithms in autonomous racing, where the vehicles are driving at high speed in an adversarial environment. We evaluate several autonomous racing path planners, finding numerous collisions during overtake maneuvers in all planners. The focused RRT search finds several times more crashes than the random strategy, and, for certain planners, tens to hundreds of times more crashes in the second half of the track.
... The lack of detailed understanding of what knowledge DNNs possess makes it difficult to fully rely on them in many safetycritical domains such as navigation of autonomous cars and 1 Arguably, it would be more precise to use the term 'grey box' to describe DNNs, as 'black box' implies that the system is intransparent and its insides might be completely inaccessible [67] , whereas 'grey box' refers to a system that is less transparent than 'white box', but for which we still have some limited knowledge of its inner workings and fundamental principles it is built upon [12,40] . However, taking into account the prevalence of the term 'black box' in ML literature as well as the connotations of mystery evoked by 'black' but not by 'grey', it is unlikely that the term 'grey box' will gain currency in the AI community. ...
Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of tasks, in particular the ones dealing with the perceptual data. However, full-scale application of DNNs in safety-critical areas is hindered by their black box-like nature, which makes their inner workings nontransparent. As a response to the black box problem, the field of explainable artificial intelligence (XAI) has recently emerged and is currently rapidly growing. The present survey is concerned with perturbation-based XAI methods, which allow to explore DNN models by perturbing their input and observing changes in the output. We present an overview of the most recent research focusing on the differences and similarities in the applications of perturbation-based methods to different data types, from extensively studied perturbations of images to the just emerging research on perturbations of video, natural language, software code, and reinforcement learning entities.
Injection of faults has been studied in various research works since last decades. Several hardware targets have been studied with respect to the efficiency of fault injections. In this paper we address the security evaluation of embedded systems in constrained environments called black-box analyses. This is not considered by standards of evaluation as they require conducting the analysis in the most relaxed conditions, often called white-box analysis which focuses on specific security modules provided that the finer details are available. However, black-box analysis has a much larger view by focusing on all the system as potential target. It is closer to a real world attacker. This allows measuring the impact of real attack scenarios, and therefore thinking and building the most adequate protections. We put forward a six steps evaluation methodology along with a practical use-case on a real end-user device. This shall give a better understanding and also an evaluation framework of black-box analysis.
In recent decades, building design and operation have been an important field of study, due to the significant share of buildings in global primary energy consumption and the time that most people spend indoors. As such, multiple studies focus on aspects of building energy consumption and occupant comfort optimization. The scientific community has discerned the importance of operation optimization through retrofitting actions for on-site building energy systems, achieved by the use of simulation techniques, surrogate modeling, as well as the guidance of existing building performance and indoor occupancy standards. However, more knowledge should be attained on the matter of whether this methodology can be extended towards the early stages of thermal system and/or building design. To this end, the present study provides a building thermal system design optimization methodology. A data set of minimum thermal system power, for a typical range of building characteristics, is generated, according to the criterion of occupant discomfort in degree hours. Respectively, a surrogate model, providing a configurable correlation of the above set of thermal system dimensioning solutions is developed, using regression model fitting techniques. Computational results indicate that such a model could provide both desirable calculative simplification and accuracy on par with existing respective thermal load calculation standards and simplified system dimensioning methods.
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Automated testing is a way to test the software system using an automation tool. Automated testing was proposed to reduce manual efforts. In this paper , manual testing model and automated testing model have been discussed. These models have been used on web based applications. There were a lot of drawbacks in manual testing ; to overcome these drawbacks automated testing has been performed. A description about the automation tool selenium has been given in this paper. In this paper ,there is a comparison between manual testing and automated testing. And also a trial has been made to show the impacts of automation in testing field.
Manufacturing advanced fiber-reinforced polymer composite structures e.g. in aerospace sector is known to be a highly complex task, subjected to a stringent framework of process qualifications and structural substantiation. Conversely, Industry 4.0 is an emerging set of sensing, network and predictive technologies and tools which can enable better decision-making towards risk reduction, supported by data-driven models, representing a novel and fruitful intersection between information technologies and manufacturing. This study aims to investigate and develop innovative applications of artificial intelligence, machine learning and data-driven approaches in support of manufacturing advanced composites, including the model building process of analyses, optimization, inverse problems and experimental investigations. This article presents an Industry 4.0-based composite process modeling pipeline, including both dataset- and model-based knowledge engineering activities pertinent to industrial considerations, with the novel approach of integrating domain expertise directly into the dataset and model building activities. This is investigated through a case study on the autoclave cure process of the Hexcel AS4/8552 carbon prepreg material. In particular, surrogate and highly interpretable models such as Logistic Rule Regression are combined with domain expert knowledge under a fuzzy scoring to provide a tool for assessing the quality (knowledgeability) of complex manufacturing datasets, prior to training black-box models. Further, specificity as a global indicator is combined with the novel Decision Boundary Crispness Score (DBSC) as a local indicator, to better quantify the overall confidence of the users in the model's prediction in the context of small perturbations in manufacturing conditions. Results demonstrate that the DBSC yields a more granular and conservative assessment of both dataset and model quality. Such explainability and local assessment features is deemed vital for production engineers in practice, especially when deploying black-box models for high-risk, in-situ decision-making.
- Christopher Lange
Lastspitzen verursachen hohe und unvorhersehbare Belastungen der Stromnetze sowie erhöhte Übertragungsverluste in den Verteilnetzen. Die Kompensation kurzzeitig hoher Nachfragen nach elektrischer Energie erfordert zudem ineffiziente und teure Spitzenlastkraftwerke sowie entsprechend dimensionierte Netzkomponenten. Um einen gleichmäßigeren Bezug größerer Stromabnehmer zu erreichen, werden seitens der Netzbetreiber finanzielle Anreize gesetzt. Dazu gehört der Leistungspreis, welcher mit der im Abrechnungszeitraum maximal aufgetretenen Leistung verrechnet wird. Eine Reduktion der Lastspitzen eröffnet Industrie- und Gewerbebetrieben dadurch hohe Einsparpotentiale. In der vorliegenden Arbeit werden das Vorgehen und die Anwendung einer Energiesektoren-übergreifenden Reduktion von Lastspitzen sowie der Einfluss auf das zugrunde liegende Energiesystem untersucht. Im Gegensatz zur bestehenden Literatur werden neben elektrischen Speichern zusätzlich Anlagen aus dem thermischen Bereich für die Lastspitzenreduktion verwendet, um das Reduktionspotential zu erhöhen. Dazu gehören Versorgungsanlagen aus der Wärme- und Kältetechnik, welche häufig einen Bestandteil von industriellen Energiesystemen darstellen. Schwerpunktmäßig werden ein Blockheizkraftwerk mit Wärmespeicher sowie eine Kälteerzeugungsanlage mit Kältespeicher betrachtet. Die Vorgehensweise ist dabei auch auf andere Komponenten übertragbar. Die Energiespeicher ermöglichen die nötige Flexibilität, um eine zusätzliche Nutzung der Anlagen für die Lastspitzenreduktion zu erreichen. Die Einzelkomponenten (Anlagen, Speicher und Peripherie) werden mit Hilfe von datenbasierten Modellen abgebildet, um eine simulative und somit nicht-invasive Untersuchung der Energiesektoren-übergreifenden Lastspitzenreduktion zu ermöglichen. Für den Einsatz der Anlagen zur Lastspitzenreduktion werden Algorithmen und Betriebsstrategien entwickelt, welche die Notwendigkeit einer Lastspitzenreduktion erkennen, Sollwerte berechnen sowie den Normal- und Lastspitzenbetrieb sicherstellen. Im Vergleich zum Stand der Wissenschaft und Technik werden dabei alle relevanten Charakteristika der Komponenten (z. B. Anfahrvorgänge) vollständig berücksichtigt. Die Betriebsstrategien werden als Zustandsautomaten formuliert, um eine Realisierung auf realen Anlagensteuerungen zu ermöglichen. Die Methoden lassen sich zudem auf in der vorliegenden Arbeit nicht betrachtete Bereiche übertragen, wie z. B. auf Druckluftkompressoren mit Druckluftspeicher. Das übergeordnete Ziel ist dabei die Einhaltung einer vorgegebenen Bezugsgrenze. Diese kann auch zeitlich variabel sein, was die Anwendung von individuellen Netzentgelten, insbesondere der atypischen Netznutzung, ermöglicht. Auch die Erreichung einer Jahresbenutzungsdauer von 7000 Stunden im Kontext der intensiven Netznutzung wird thematisiert. Die Modelle und Betriebsstrategien werden in einer erweiterbaren und flexiblen Simulationsumgebung zusammengeführt und verknüpft, um konkrete Szenarien darzustellen und zu untersuchen sowie die Komponenten und Parameter zu optimieren. Innerhalb der Simulation werden die Komponenten dynamisch über eine Netzliste verknüpft, was Erweiterungen stark vereinfacht und dadurch umfangreiche Untersuchungen ermöglicht. Die Simulationen zeigen, dass zur zuverlässigen Einhaltung einer Bezugsgrenze stets eine Batterie notwendig ist, da diese eine schnelle Reaktionszeit sowie eine stufenlose Ausgangsleistung aufweist. Das Batteriesystem wird im ersten Szenario isoliert betrachtet. Für die Parameter eines Referenzsystems, welches Lastspitzen in der Größenordnung von einem Megawatt aufweist, wird ein Reduktionspotential von etwa 10 % ermittelt. Dies führt zu einer Amortisationszeit von unter fünf Jahren. Bei der zusätzlichen Berücksichtigung eines Blockheizkraftwerks mit Wärmespeicher steigt dieser Wert auf ca. 18 %. In Kombination mit einem Kältespeicher wird das Potential weiter gesteigert auf 21 %. Bei einem Leistungspreis von 100 Euro pro Kilowatt führt das zu einer jährlichen Ersparnis von etwa 21 Tausend Euro. Ein zweiter Jahresdatensatz aus dem Referenzsystem zeigt einen ähnlichen Einfluss der Maßnahmen auf die Gesamtersparnis. Wird auch der wärmegeführte Betrieb des BHKW berücksichtigt, liegt die Ersparnis in einer Größenordnung von 139 Tausend Euro pro Jahr, was in einer Amortisationszeit von unter drei Jahren resultiert. Da die Ergebnisse stark von den Anlagendimensionen abhängig sind, wird zudem eine Methode zur Berechnung des Reduktionspotentials unter Variation der Nennleistungen und -kapazitäten gezeigt und für zahlreiche Parametersätze angewandt. Die Betriebsstrategien wurden im Referenzsystem implementiert, welches Messergebnisse für die Validierung der Simulationen bereitstellt. Die Simulationen zeigen im Vergleich zu den Messungen sehr geringe Abweichungen in der resultierenden Trafoleistung bei einem mittleren absoluten Fehler von maximal vier Kilowatt. Die vorliegende Arbeit liefert somit die Vorgehensweise für die Planung und Umsetzung einer erfolgreichen Energiesektoren-übergreifenden Lastspitzenreduktion und beleuchtet die exemplarische Anwendung der Dimensionierung von Komponenten, Optimierung von Algorithmusparametern sowie die Implementierung der Betriebsstrategien in einem realen System.
- Mohd Ehmer Khan
Software testing is the process of analyzing software to find the difference between required and existing condition. Software testing is performed throughout the development cycle of software and it is also performed to build quality software, for this purpose two basic testing approaches are used, they are white box testing and black box testing. One of the software testing technique which I have explain in my paper is Black Box Testing, it is a method of generating test cases that are independent of software internal structure, I have also briefly explore various different approaches to black box testing technique for finding errors. Since black box testing is always based either directly or indirectly on the software specification so it is also called specification based testing.
- Mohd. Ehmer Khan
Software testing is an activity which is aimed for evaluating an attribute or capability of a program and ensures that it meets the required result. There are many approaches to software testing, but effective testing of complex product is essentially a process of investigation, not merely a matter of creating and following route procedure. It is often impossible to find all the errors in the program. This fundamental problem in testing thus throws open question, as to what would be the strategy that we should adopt for testing. Thus, the selection of right strategy at the right time will make the software testing efficient and effective. In this paper I have described software testing techniques which are classified by purpose.
- Farmeena Khan
Farmeena Khan I completed my BCA in 2003 from Dr. B. R. Ambedkar University, Agra, India and MBA in 2011 from Janardan Rai Nagar Rajasthan Vidyapeeth University, Rajasthan, India, and pursuing M.Sc (Computer Science) from EIILM University, Jorethang, Sikkim, India.
Black Box Testing With No Knowledge of the System
Source: https://www.researchgate.net/publication/270554162_A_Comparative_Study_of_White_Box_Black_Box_and_Grey_Box_Testing_Techniques