TESTING WEB APPLICATIONS USING CLUSTERS

• US 20180239693A1
• Filed: 02/21/2017
• Published: 08/23/2018
• Est. Priority Date: 02/21/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ben-Bassat, Ilan, Dubnikov, Daniel, Kedmi, Sagi, Rokah, Erez
• Granted Patent: US 10,346,291 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/3672 Test management
• CPC Class Codes: G06F 11/3676 for coverage analysis
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]

Abstract

An example system includes a processor to crawl a plurality of web pages of a web application to be tested. The processor is also configured to receive an intercepted input to the web application and an output from a web application associated with each crawled web page. The processor is to further configured to detect testable elements in the intercepted input and the output. The processor is also configured to generate a fingerprint for each web page based on the detected testable elements. The processor is also configured to generate a list of clusters comprising one or more similar web pages based on the fingerprints. The processor is configured to test a single web page from each cluster.

Claims

1. A system, comprising:

a processor configured with code to;

crawl a plurality of web pages of a web application to be tested;

receive an intercepted input to the web application and an output from the web application associated with each crawled web page;

detect testable elements in the intercepted input and the output;

generate a fingerprint for each web page based on the detected testable elements;

generate a list of clusters comprising one or more similar web pages based on the fingerprints; and

test a single web page from each cluster.

2. The system of claim 1, wherein the input comprises an HTTP request and the output comprises an HTTP response.

3. The system of claim 1, wherein the input comprises a GET parameter and the output comprises a document object model.

4. The system of claim 1, wherein the processor is configured to calculate a similarity score between a fingerprint for a web page from the plurality of web pages and a cluster from the list of clusters and add the web page to the cluster in response to detecting that the similarity score exceeds a similarity threshold.

5. The system of claim 1, wherein the processor is configured to calculate a similarity score between the fingerprint for each web pages and each cluster in the list of clusters based on a calculated hamming distance.

6. The system of claim 1, wherein the fingerprint for each web page comprises a plurality of response element fingerprints and a plurality of request element fingerprints.

7. The system of claim 1, wherein the processor is further configured to:

generate a maximal distance between requests for each cluster in the list of clusters;

receive a request to be sent to the web application;

detect that the request would not result in a web page that belongs to any cluster based on the maximal distances for the clusters; and

send the request to the web application in response to detecting that the request would not result in the web page that belongs to any cluster.

8. A computer-implemented method, carried our by one or more processors executed with code, comprising:

crawling, via a processor, a plurality of web pages of a web application to be tested;

receiving, via a processor, an intercepted input to the web application and an output from the web application associated with each crawled web page;

detecting, via a processor, testable elements in the intercepted input and the output;

generating, via a processor, a fingerprint for each web page based on the detected testable elements;

generating, via a processor, a list of clusters comprising one or more similar web pages based on the fingerprints; and

testing, via a processor, a single web page from each cluster.

9. The computer-implemented method of claim 8, comprising calculating, via the processor, a similarity score between a fingerprint for a web page from the plurality of web pages and a cluster from the list of clusters and add the web page to the cluster in response to detecting that the similarity score exceeds a similarity threshold.

10. The computer-implemented method of claim 8, comprising calculating, via a processor, a similarity score between the fingerprint for each web pages and each cluster in the list of clusters based on a calculated hamming distance.

11. The computer-implemented method of claim 8, wherein generating the fingerprint for each web page comprises generating a fingerprint for each element in a request resulting in the web page and a fingerprint for each element in the web page and combining the fingerprints for the elements to generate the fingerprint for each web page.

12. The computer-implemented method of claim 8, further comprising intercepting the input to the web application, sending the input to the web application if the input would not result in a web page that belongs to any cluster based on the maximal distances calculated for list of clusters, and receiving an output from the web application in response to the input.

13. The computer-implemented method of claim 8, comprising generating a maximal distance between requests for a cluster by calculating similarity scores between requests resulting in the web pages in the cluster, wherein the maximal distance comprises a similarity score that is lower than other similarity scores.

14. The computer-implemented method of claim 8, comprising:

generating, via a processor, a maximal distance for each cluster in the list of clusters;

receiving, via a processor, a request to be sent to the web application;

detecting, via a processor, that the request would not result in a web page that belongs to any cluster based on the maximal distances for the clusters; and

sending, via a processor, the request in response to detecting that the request would not result in the web page that belongs to any cluster.

15. A computer program product for testing web applications, the computer program product comprising a computer-readable storage medium having program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program code executable by one or more processors to:

crawl a plurality of web pages of a web application to be tested;

receive an intercepted input to the web application and an output from the web application associated with each crawled web page;

detect testable elements in the intercepted input and the output;

generate a fingerprint for each web page based on the detected testable elements;

generate a list of clusters comprising one or more similar web pages based on the fingerprints; and

test a single web page from each cluster.

16. The computer program product of claim 15, comprising program code executable by a processor to calculate a similarity score between a fingerprint for a web page from the plurality of web pages and a cluster from the list of clusters and add the web page to the cluster in response to detecting that the similarity score exceeds a similarity threshold.

17. The computer program product of claim 15, comprising program code executable by a processor to calculate a similarity score between the fingerprint for each web pages and each cluster in the list of clusters based on a calculated hamming distance.

18. The computer program product of claim 15, comprising program code executable by a processor to:

generate a fingerprint for each element in a request resulting in the web page and a fingerprint for each element in the web page; and

combine the fingerprints for the elements to generate the fingerprint for each web page.

19. The computer program product of claim 15, comprising program code executable by a processor to generate a maximal distance between requests for a cluster by calculating similarity scores between requests resulting in the web pages in the cluster, wherein the maximal distance comprises a similarity score that is lower than other similarity scores.

20. The computer program product of claim 15, comprising program code executable by a processor to:

generate a maximal distance between requests for each cluster in the list of clusters;

receive a request to be sent to the web application;

detect that the request would not result in a web page that belongs to any cluster based on the maximal distances for the clusters; and

send the request in response to detecting that the request would not result in the web page that belongs to any cluster.