I have gone through many articles and all are suggested me to create custom fields/Keywords in testlink to estimate the time for sprint execution.
Articles like :-
http://www.softwaretestingconcepts.com/testlink-using-custom-fields-and-keywords-for-effective-testing
Is there any alternative approach or any scientific method to estimate your sprint execution accurately.
I have found one article proposing below method:-
Number of Test Cases = (Number of Function Points) × 1.2
Source :- http://www.tutorialspoint.com/estimation_techniques/estimation_techniques_testing.htm
What should be the approach to estimating your execution cycle? Currently, I am doing it as per my experience in my project. It is working fine but management wants a concrete mechanism for same. Please suggest and share your experience
I have added Time Estimate and Actual Time from below option:-
Below is the result of above setting
I am not able to get this field data in report. I need total estimate also and then comparison between actual and estimated time
Any help would be appreciated
TestLink provides inbuilt support to record Estimated Exec. time(define at TestCase creation) and Actual Execution time. (record at TestCase Execution time)
Based on this feature hopes you can buildup your requirement.
Related
I am trying google cloud's dataflow service which is useful for efficient computation time. My code has the following programming model for the dataflow pipeline:
start=(p | "read" >> beam.io.ReadFromText("gcs path"))
end= start | "data_generation" >> beam.Pardo(PerfromFunction)
What I am doing:
PerformFunction is a regular Python function which contains a few series of functions for data-generation purpose. My problem is that when I run this function on a regular VM of n1-standard-16 on a single processor, it takes around 1 hour to complete the whole process.
Why I opted Dataflow:
I then decided to go for Dataflow where a ParDo function performs Multi-Threading of the given function and obviously to reduce the computational time from 1 hour to less than 1 hour.
The Problem:
After running a Dataflow job with the above-mentioned programming model, I came to realize that Dataflow is still taking around 1 hour to complete the entire process which is mentioned as wall-time on the GCP Dataflow UI. I then logged in to the worker machine and saw the resource utilization using the command htop and found that the machine was only utilizing one processor with 60% average usage.
Expected Results or Suggestions:
1. Can multiprocessing be done in the Dataflow worker Cluster?
2. Is my programming model very limited and wrong?
3. ParDo function does not seem to reduce the computational time as expected, What do you think I am doing wrong here?
PS- Owing to some protocols, I can not share the code. Thank you for understanding. Also please correct me if I wrongly understand dataflow at some point.
Apache Beam, and Dataflow are able to parallelize your computation based on the input that comes into it.
If you have a single computation to apply, and this computation takes one hour, then Beam will not be able to speed up your computation. Beam can help you if you need to apply the same computation multiple times to different elements (or data points).
You should also consider things such as overhead of running the computation in a distributed fashion (data copying, network calls, etc).
So, to be able to answer your question: How may individual "data points" (how many lines) are there in your GCS file? Is it possible to parallelize the computation over each one? How long does it take to process each one?
I'm importing text items to Google's AutoML. Each row contains around 5000 characters and I'm adding 70K of these rows. This is a multi-label data set. There is no progress bar or indication of how long this process will take. Its been running for a couple of hours. Is there any way to calculate time remaining or total estimated time. I'd like to add additional data sets, but I'm worried that this will be a very long process before the training even begins. Any sort of formula to create even a semi-wild guess would be great.
-Thanks!
I don't think that's possible today, but I filed a feature request [1] that you can follow for updates. I asked for both training and importing data, as for training it could be useful too.
I tried training with 50K records (~ 300 bytes/record) and the load took more than 20 mins after which I killed it. I retried with 1K, which ran for 20 mins and then emailed me an error message saying I had multiple labels per input (yes, so what? training data is going to have some of those) and I had >100 labels. I simplified the classification buckets and re-ran. It took another 20 mins and was successful. Then I ran 'training' which took 3 hours and billed me $11. That maps to $550 for 50K recs, assuming linear behavior. The prediction results were not bad for a first pass, but I got the feeling that it is throwing a super large neural net at the problem. Would help if they said what NN it was and its dimensions. They do say "beta" :)
don't wast your time trying to using google for text classification. I am a GCP hard user but microsoft LUIS is far better, precise and so much faster that I can't believe that both products are trying to solve same problem.
Luis has a much better documentation, support more languages, has a much better test interface, way faster.. I don't know if is cheaper yet because the pricing model is different but we are willing to pay more.
I'm using the AWS Machine Learning regression to predict the waiting time in a line of a restaurant, in a specific weekday/time.
Today I have around 800k data.
Example Data:
restaurantID (rowID)weekDay (categorical)time (categorical)tablePeople (numeric)waitingTime (numeric - target)1 sun 21:29 2 23
2 fri 20:13 4 43
...
I have two questions:
1)
Should I use time as Categorical or Numeric?
It's better to split into two fields: minutes and seconds?
2)
I would like in the same model to get the predictions for all my restaurants.
Example:
I expected to send the rowID identifier and it returns different predictions, based on each restaurant data (ignoring others data).
I tried, but it's returning the same prediction for any rowID. Why?
Should I have a model for each restaurant?
There are several problems with the way you set-up your model
1) Time in the form you have it should never be categorical. Your model treats times 12:29 and 12:30 as two completely independent attributes. So it will never use facts it learn about 12:29 to predict what's going to happen at 12:30. In your case you either should set time to be numeric. Not sure if amazon ML can convert it for you automatically. If not just multiply hour by 60 and add minutes to it. Another interesting thing to do is to bucketize your time, by selecting which half hour or wider interval. You do it by dividing (h*60+m) by some number depending how many buckets you want. So to try 120 to get 2 hr intervals. Generally the more data you have the smaller intervals you can have. The key is to have a lot of samples in each bucket.
2) You should really think about removing restaurantID from your input data. Having it there will cause the model to over-fit on it. So it will not be able to make predictions about restaurant with id:5 based on the facts it learn from restaurants with id:3 or id:9. Having restaurant id there might be okay if you have a lot of data about each restaurant and you don't care about extrapolating your predictions to the restaurants that are not in the training set.
3) You never send restaurantID to predict data about it. The way it usually works you need to pick what are you trying to predict. In your case probably 'waitingTime' is most useful attribute. So you need to send weekDay, time and number of people and the model will output waiting time.
You should think what is relevant for the prediction to be accurate, and you should use your domain expertise to define the features/attributes you need to have in your data.
For example, time of the day, is not just a number. From my limited understanding in restaurant, I would drop the minutes, and only focus on the hours.
I would certainly create a model for each restaurant, as the popularity of the restaurant or the type of food it is serving is having an impact on the wait time. With Amazon ML it is easy to create many models as you can build the model using the SDK, and even schedule retraining of the models using AWS Lambda (that mean automatically).
I'm not sure what the feature called tablePeople means, but a general recommendation is to have as many as possible relevant features, to get better prediction. For example, month or season is probably important as well.
In contrast with some answers to this post, I think resturantID helps and it actually gives valuable information. If you have a significant amount of data per each restaurant then you can train a model per each restaurant and get a good accuracy, but if you don't have enough data then resturantID is very informative.
1) Just imagine what if you had only two columns in your dataset: restaurantID and waitingTime. Then wouldn't you think the restaurantID from the testing data helps you to find a rough waiting time? In the simplest implementation, your waiting time per each restaurantID would be the average of waitingTime. So definitely restaurantID is a valuable information. Now that you have more features in your dataset, you need to check if restaurantID is as effective as the other features or not.
2) If you decide to keep restaurantID then you must use it as a categorical string. It should be a non-parametric feature in your dataset and maybe that's why you did not get a proper result.
On the issue with day and time I agree with other answers and considering that you are building your model for the restaurant, hourly time may give a more accurate result.
I have to perform a data mining task on a database containing informations about insurance policies. Each tuple indicates data about a single policy, along with information regarding the agency that issued it, the customer it is referring to and other fields. It is like a product between hypotetical tables Policies, Customers and Agencies. The fields are the following:
Policy Type,ID Number,Policy Status ,Product Description,Product Combinations,Issue Date,Effective Date,Maturity Date,Policy Duration,Loan Duration ,Cancellation Date ,Reason for cancellation,Total Premium,Splitter Premium,ID Partners,ID Agency,Country Agency,ID Zone,Agency potential,Sex Contractor ,Birth Year Contractor,Job Contractor,Sex Insured,Job Insured,Birth Year Insured,Product Area,Legal Form,ID Claim,Year Claim,Status Claim,Provision Claim,Payments Claim
This is an academic task and our professor wants us to identify churn rates, cross-selling and up-selling. I am not quite into the field and therefore I sought those terms on wikipedia. I started with churn rate and it appears to me that in this case I have to characterize the properties of customers whose Policy Status is set to "canceled" and the Reason for cancellation is "customer cancellation".
With Rapid Miner, I tried to apply decision trees and rule mining, but the subset of interest is so small that the output model, despite having a good accuracy overall, has a very very very poor accuracy in predicting canceled policies. This happens because the subset of canceled policies is really small. I also tried to apply the MetaCost operator with a given cost matrix in which the cost of misclassifying canceled policies is outrageously high with respect to the others (like a million times higher), but this did not change the result at all.
My best option now is to use the sequential covering algorithm for rule mining, but rapid miner does not implement it and I would have to code it manually.
Do you have any suggestion on how to build a good model for that small subset of canceled policies, so that we could use it to identify customers that would potentially cancel their policy in the future?
N.B.: since it comes from a real source, albeit anonymized, I cannot disclose the database or any data contained within.
Did you try Navie Bayes? It works well with small set of data. You can as well try a variant of it like AODE. AODE is not available in Rapid Miner. You should install Weka extension to access AODE in Rapid Miner.
You need to balance your dataset, so that the classes (cancelled / not cancelled) are the same size. This means (temporarily) discarding lots of data.
You can use the Sample operator with the Balance Labels checkbox to do this.
I am currently working on a project that requires load testing of web services.
One of the services is being called 60,000 times in the production during Busy-Day/Busy-HR.
{PerfTest Env=PROD}
Input Account Number
Output AccountDetails
Do I really need 60,000 unique account numbers(TEST DATA) for this loadrunner script to simulate the production scenario?
If unique data is required, for endurance test I will have to prepare lot of test data for each web service.
If I don't get that much test data, what is the chance of Load Test being affected due to Application Server Cache mechanism??
Can somebody help me?
Thanks
Ram
Are you simulating a day or the highest volume hour in the last year? This can help you to shape the amount of data that you need. Rarely would you start with a 24 hour test. Instead you would be looking at your high water test of an hour with a ramp up and ramp down, so you would need approximately 1.333* your high water hour's worth of data.
So this can drop your 60K to (potentially) 20K(?) I am making an assumption that your worst hour over the last year is somewhere around 1/3 of your traditional day. I have observed this pattern over and over again in different environments over the past two decades. You will want to objectively verify this with log data or query data to support the number in your environment.
Next up, how many of these inquiries are actually unique? You are really going to need a log of the queries across a day (or your high water hour) to determine this. Log processing tools such as Microsoft Logparser or Splunk/Splunk Storm can help you to pull the observed distribution of unique account references within your data, including counts of those which are multiple. Once you know this you can simply use a data file with a fixed block size for each user for unique data and once the data is exhausted the user exits.