Data flow testing uses a program's control flow graph annotated with symbols like d, k, u to track the state of variables and identify anomalies. Static analysis can detect some anomalies but is insufficient on its own due to limitations in analyzing dynamic features like pointers, concurrency, and interrupts. The data flow model represents each statement as a node and links are weighted with sequences of symbols showing variable states to identify anomalies like ku that indicate bugs.
Black-box testing is a method of software testing that examines the functionality of an application based on the specifications.
White box testing is a testing technique, that examines the program structure and derives test data from the program logic/code
Black-box testing is a method of software testing that examines the functionality of an application based on the specifications.
White box testing is a testing technique, that examines the program structure and derives test data from the program logic/code
In software testing, there are many paths between the entry and exit of a software program. So it’s difficult to fully test all paths of even a simple unit. This is a challenge when we design test cases.
This lecture provide a review of requirement engineering process. The slides have been prepared after reading Ian Summerville and Roger Pressman work. This lecture is helpful to understand user, and user requirements.
This is the most important topic of OOAD named as Object Oriented Testing. It is used to prepare a good software which has no bug in it and it performs very fast. <a href="https://harisjamil.pro">Haris Jamil</a>
In software testing, there are many paths between the entry and exit of a software program. So it’s difficult to fully test all paths of even a simple unit. This is a challenge when we design test cases.
This lecture provide a review of requirement engineering process. The slides have been prepared after reading Ian Summerville and Roger Pressman work. This lecture is helpful to understand user, and user requirements.
This is the most important topic of OOAD named as Object Oriented Testing. It is used to prepare a good software which has no bug in it and it performs very fast. <a href="https://harisjamil.pro">Haris Jamil</a>
Concurrency in Distributed Systems : Leslie Lamport papersSubhajit Sahu
In computer science, concurrency is the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the final outcome. This allows for parallel execution of the concurrent units, which can significantly improve overall speed of the execution in multi-processor and multi-core systems. In more technical terms, concurrency refers to the decomposability property of a program, algorithm, or problem into order-independent or partially-ordered components or units.[1]
A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi, the parallel random-access machine model, the actor model and the Reo Coordination Language.
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La scrittura di test automatici nello sviluppo software è ormai di fondamentale importanza, in quanto permette di:
1. Individuare e correggere molto prima, già in fase di sviluppo, i bug.
2. Sviluppare e testare più velocemente il codice, riducendo di molto le volte in cui bisogna ricorrere al debugger.
3. Essere molto più confidenti che una modifica fatta ad un "vecchio" pezzo di codice non "rompa" tutto il resto e non funzioni più niente (ovviamente scoprendolo quando ormai si è rilasciato in produzione!).
Questi sono "solo" 3 di una quindicina di benefici che sono riuscito ad elencare, ottenibili utilizzando una pratica durante lo sviluppo del codice: la scrittura di test automatici.
Con questo workshop vogliamo introdurre gli sviluppatori ai test automatici, una pratica purtroppo non ancora conosciuta e utilizzata quanto meriterebbe, che può cambiare radicalmente il modo con cui scriviamo il codice, portandolo verso un approccio più "ingegneristico".
Faremo una panoramica sulle varie tipologie di test e sui benefici che possono portare, approfondendo in particolare i test unitari (unit test) e d'integrazione (integration test).
I test automatici sono un argomento trasversale ai linguaggi di programmazione, perciò potrete seguire il workshop a prescindere da quale linguaggio utilizziate.
Recommender Systems from A to Z – The Right DatasetCrossing Minds
In the last years a lot of improvements were done in the field of Machine Learning and the Tools that support the community of developers. But still, implementing a recommender system is very hard.
That is why at Crossing Minds, we decided to create a series of 4 meetups to discuss how to implement a recommender system end-to-end:
Part 1 – The Right Dataset
Part 2 – Model Training
Part 3 – Model Evaluation
Part 4 – Real-Time Deployment
This first meetup will be about building the right dataset and doing all the preprocessing needed to create different models. We will talk about explicit vs implicit feedback, dataset analysis, likes/dislikes vs ratings, users and items features, normalization and similarities.
A presenetation on basics of software testing, explaining the software development life cycle and steps invovled in it and detials about each step from the testing point of view.
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Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
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Globus Connect Server Deep Dive - GlobusWorld 2024Globus
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Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
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COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
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Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
2. • Data flow testing uses the control flowgraph to explore the unreasonable
things that can happen to data (i.e., anomalies).
• Consideration of data flow anomalies(inconsistency) leads to test path
selection strategies that fill the gaps between complete path testing and
branch or statement testing.
Data Flow Testing
3. • Data-flow testing is the name given to a family of test strategies based on
selecting paths through the program’s control flow in order to explore
sequences of events related to the status of data objects.
• E.g., Pick enough paths to assure that:
Every data object has been initialized prior to its use.
All defined objects have been used at least once.
Data Flow Testing (Cont’d)
4. There are two types of data flow machines with different architectures.
• Von Neumann machines
• Multi-instruction, multi-data machines (MIMD).
Data Flow Machines
5. • This architecture features interchangeable storage of instructions and data
in the same memory units.
• The Von Neumann machine Architecture executes one instruction at a time
in the following, micro instruction sequence:
Fetch instruction from memory
Interpret instruction
Fetch operands
Process or Execute
Store result
Increment program counter
GOTO 1
Von Neumann Machine Architecture
6. Multi-instruction, Multi-data machines (MIMD) Architecture
• These machines can fetch several instructions and objects in parallel.
• They can also do arithmetic and logical operations simultaneously on
different data objects.
• The decision of how to sequence them depends
on the compiler.
7. Bug Assumption
• The bug assumption for data-flow testing strategies is that
• control flow is generally correct and that something has gone wrong
with the software so that data objects are not available
• if there is a control-flow problem, we expect it to have symptoms that
can be detected by data-flow analysis.
• Although we'll be doing data-flow testing, we won't be using data flow
graphs as such. Rather, use an ordinary control flowgraph annotated to
show what happens to the data objects of interest at the moment.
8. Data Flow Graphs
• The data flow graph is a graph consisting of nodes and directed links.
• We will use an control graph to show what happens to data objects of
interest at that moment.
• Our objective is to expose deviations between the data flows we have and
the data flows we want.
10. Bug Assumption
• The bug assumption for data-flow testing strategies is that
• control flow is generally correct and that something has gone wrong
with the software so that data objects are not available
• if there is a control-flow problem, we expect it to have symptoms that
can be detected by data-flow analysis.
• Although we'll be doing data-flow testing, we won't be using data flow
graphs as such. Rather, use an ordinary control flowgraph annotated to
show what happens to the data objects of interest at the moment.
11. Data Object State and Usage
• Data Objects can be created, killed and used.
• They can be used in two distinct ways:
1. In a Calculation 2. As a part of a Control Flow Predicate.
• The following symbols denote these possibilities:
• Defined: d - defined, created, initialized etc.
• Killed or undefined: k - killed, undefined, released etc
• Usage: u - used for something (c - used in Calculations, p - used in a
predicate)
12. 1. Defined (d)
• An object is defined explicitly when it appears in a data declaration.
• Or implicitly when it appears on the left hand side of the assignment.
• It is also to be used to mean that a file has been opened.
• A dynamically allocated object has been allocated.
• Something is pushed on to the stack.
• A record written.
13. 2. Killed or Undefined (k)
• An object is killed on undefined when it is released or otherwise made unavailable.
• When its contents are no longer known with certitude (with aboslute certainity /
perfectness).
• Release of dynamically allocated objects back to the availability pool.
• Return of records.
• The old top of the stack after it is popped.
• An assignment statement can kill and redefine immediately.
For example, if A had been previously defined and we do a new assignment such as
A : = 17, we have killed A's previous value and redefined A
14. 3. Usage (u)
• A variable is used for computation (c) when it appears on the right hand
side of an assignment statement.
• A file record is read or written.
• It is used in a Predicate (p) when it appears directly in a predicate.
15. Data Flow Anomalies
• An anomaly is denoted by a two-character sequence of actions.
• For example,
• ku means that the object is killed and then used, where as
• dd means that the object is defined twice without an intervening
usage.
16. Data Flow Anomalies (Cont’d)
• What is an anomaly is depend on the application.
• There are nine possible two-letter combinations for d, k and u. some are
bugs, some are suspicious, and some are okay.
• dd :- probably harmless but suspicious. Why define the object twice without an intervening usage?
• dk :- probably a bug. Why define the object without using it?
• du :- the normal case. The object is defined and then used.
• kd :- normal situation. An object is killed and then redefined.
• kk :- harmless but probably buggy. Did you want to be sure it was really killed?
• ku :- a bug. the object doesnot exist.
• ud :- usually not a bug because the language permits reassignment at almost any time.
• uk :- normal situation.
• uu :- normal situation.
17. Data Flow Anomalies (Cont’d)
• In addition to the two letter situations, there are six single letter situations.
• We will use a leading dash to mean that nothing of interest (d,k,u) occurs
prior to the action noted along the entry-exit path of interest.
• A trailing dash to mean that nothing happens after the point of interest to
the exit.
18. Data Flow Anomalies (Cont’d)
• They possible anomalies are:
• -k :- possibly anomalous because from the entrance to this point on the path, the variable had not been
defined. We are killing a variable that does not exist.
• -d :- okay. This is just the first definition along this path.
• -u :- possibly anomalous. Not anomalous if the variable is global and has been previously defined.
• k- :- not anomalous. The last thing done on this path was to kill the variable.
• d- :- possibly anomalous. The variable was defined and not used on this path. But this could be a global
definition.
• u- :- not anomalous. The variable was used but not killed on this path. Although this sequence is not
anomalous, it signals a frequent kind of bug. If d and k mean dynamic storage allocation and return
respectively, this could be an instance in which a dynamically allocated object was not returned to the
pool after use.
19. Data Flow Anomaly State Graph
• Data flow anomaly model prescribes that an object can be in one of four
distinct states:
• K :- undefined, previously killed, doesn't exist
• D :- defined but not yet used for anything
• U :- has been used for computation or in predicate
• A :- anomalous
20. Data Flow Anomaly State Graph (Cont’d)
• These capital letters (K,D,U,A) denote the state of the variable and should
not be confused with the program action, denoted by lower case letters.
• Unforgiving Data - Flow Anomaly Flow Graph: Unforgiving model, in which
once a variable becomes anomalous it can never return to a state of grace.
21. Unforgiving Data Flow Anomaly State Graph
• Assume that the variable starts in the K state - that is, it has not been
defined or does not exist. If an attempt is made to use it or to kill it (e.g.,
say that we're talking about opening, closing, and using files and that
'killing' means closing), the object's state becomes anomalous (state A)
and, once it is anomalous, no action can return the variable to a working
state. If it is defined (d), it goes into the D, or defined but not yet used,
state. If it has been defined (D) and redefined (d) or killed without use (k),
it becomes anomalous, while usage (u) brings it to the U state. If in U,
redefinition (d) brings it to D, u keeps it in U, and k kills it.
22. Forgiving Data - Flow Anomaly Flow Graph
• Forgiving Data - Flow Anomaly Flow Graph: Forgiving model is an alternate
model where redemption (recover) from the anomalous state is possible.
• This graph has three
normal and three
anomalous states and
he considers the kk
sequence not to be
anomalous.
The difference between this state graph and Unforgiving data is that redemption is possible
23. STATIC Vs DYNAMIC Anomaly Detection
• Static Analysis is analysis done on source code without actually executing
it.
• E.g., Syntax errors are caught by static analysis
• Dynamic Analysis is analysis done as a program is executing and is based
on intermediate values that result from the program’s execution.
• E.g., A division by 0 error is caught by dynamic analysis.
• If a data-flow anomaly can be detected by static analysis then the anomaly
does not concern testing. (Should be handled by the compiler.)
24. Anomaly Detection Using Compilers
• Compilers are able to detect several data-flow anomalies using static
analysis.
• E.g., By forcing declaration before use, a compiler can detect anomalies
such as:
• -u
• -ku
• Optimizing compilers are able to detect some dead variables.
25. Is Static Analysis Sufficient?
• Questions:
• Why isn’t static analysis enough?
• Why is testing required?
• Could a good compiler detect all dataflow anomalies?
• Answer:
• No. Detecting all data-flow anomalies is provably unsolvable.
26. Why Static Analysis isn't enough?
• There are many things for which current notions of static analysis are
inadequate. They are:
• Dead Variables: Detecting unreachable variables is unsolvable in the
general case.
• Arrays: Dynamically allocated arrays contain garbage unless they are
initialized explicitly. (-u anomalies are possible)
• Pointers: Impossible to verify pointer values at compile time.
27. Why Static Analysis isn't enough?
• False Anomalies: Even an obvious bug
(e.g., ku) may not be a bug if the path along which the anomaly
exists is unachievable. (Determining whether a path is or is not
achievable is unsolvable.)
• Recoverable Anomalies and Alternate State Graphs: What constitutes
an anomaly depends on context, application, and semantics.
• How does the compiler know which model I have in mind?
• It can't because the definition of "anomaly" is not fundamental.
The language processor must have a built-in anomaly definition
with which you may or may not (with good reason) agree.
28. Why Static Analysis isn't enough?
• Concurrency, Interrupts, System Issues:
As soon as we get away from the simple single-task uniprocessor
environment and start thinking in terms of systems, most anomaly issues
become vastly more complicated. How often do we define or create data
objects at an interrupt level so that they can be processed by a lower-
priority routine? Interrupts can make the "correct" anomalous and the
"anomalous" correct. True concurrency (as in an MIMD machine) and
pseudoconcurrency (as in multiprocessing) systems can do the same to
us. Much of integration and system testing is aimed at detecting data-flow
anomalies that cannot be detected in the context of a single routine.
29. Why Static Analysis isn't enough?
• Although static analysis methods have limits, they are worth using and a
continuing trend in language processor design has been better static
analysis methods, especially for data flow anomaly detection. That's good
because it means there's less for us to do as testers and we have far too
much to do as it is.
30. DATA FLOW MODEL
• The data flow model is based on the program's control flow graph - Don't
confuse that with the program's data flowgraph..
• Here we annotate each link with symbols (for example, d, k, u, c, p) or
sequences of symbols (for example, dd, du, ddd) that denote the sequence
of data operations on that link with respect to the variable of interest. Such
annotations are called link weights.
• The control flow graph structure is same for every variable: it is the
weights that change.
31. Components of the model
• To every statement there is a node, whose name is unique. Every node
has at least one outlink and at least one inlink except for exit nodes and
entry nodes.
• Exit nodes are dummy nodes placed at the outgoing arrowheads of exit
statements (e.g., END, RETURN), to complete the graph. Similarly, entry
nodes are dummy nodes placed at entry statements (e.g., BEGIN) for the
same reason.
32. Components of the model (Cont’d)
• The outlink of simple statements (statements with only one outlink) are
weighted by the proper sequence of data-flow actions for that statement.
Note that the sequence can consist of more than one letter. For example,
the assignment statement A:= A + B in most languages is weighted by cd or
possibly ckd for variable A. Languages that permit multiple simultaneous
assignments and/or compound statements can have anomalies within the
statement. The sequence must correspond to the order in which the object
code will be executed for that variable.
33. • Predicate nodes (e.g., IF-THEN-ELSE, DO WHILE, CASE) are weighted with
the p - use(s) on every outlink, appropriate to that outlink.
• Every sequence of simple statements (e.g., a sequence of nodes with one
inlink and one outlink) can be replaced by a pair of nodes that has, as
weights on the link between them, the concatenation of link weights.
• If there are several data-flow actions on a given link for a given variable,
then the weight of the link is denoted by the sequence of actions on that
link for that variable.
Components of the model (Cont’d)
34. • Conversely, a link with several data-flow actions on it can be replaced by a
succession of equivalent links, each of which has at most one data-flow
action for any variable.
Components of the model (Cont’d)