METHOD OF COLLABORATIVE SOFTWARE DEVELOPMENT

• US 20160048805A1
• Filed: 08/18/2014
• Published: 02/18/2016
• Est. Priority Date: 08/18/2014
• Status: Active Grant

• Current Assignee: Drexel University
• Original Assignee: Drexel University
• Inventors: Blincoe, Kelly Coyle
• Granted Patent: US 9,799,007 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services

Abstract

A method of collaboratively developing software includes recording a plurality of developers'"'"' tasks relating to a collection of software development files via software executing on a computer. The method further includes calculating a proximity score between a plurality of tasks based on the overlap of the developers'"'"' activities via software executing on a computer. The method further includes selecting and capturing a group of task properties that along with the proximity score can be used to select a group of task pairings that require coordination. The method further includes notifying the developers assigned to the task pairings selected that they may need to coordinate their development efforts.

Claims

1. A method of collaboratively developing software, comprising:

(a) recording a plurality of developers'"'"' activities relating to a collection of software development files via software executing on a computer;

(b) calculating a proximity score between a plurality of tasks based on the overlap of the developers'"'"' activities via software executing on a computer;

(c) selecting a group of task pairings that exceed a threshold proximity score; and

(d) notifying the developers assigned to the task pairings selected in step

(c) that they need to coordinate their development efforts on the task pairings.

2. The method of claim 1, wherein the developers'"'"' activities include viewing and editing files.

3. The method of claim 1, further comprising:

(e) collecting information about code file software architecture, operating system, hardware, and/or other attributes associated with the involved software system prior to step (a);

(f) selecting a group of task pairings based on the information collected in step (e); and

(g) selecting developers to be notified in step (d) based on the groups selected in step (c) and step (f).

4. The method of claim 1, further comprising:

(e) querying the plurality of developers or system experts on which entities require collaborating and the degree to that collaboration;

(f) selecting a group of task pairings based on the information collected in step (e); and

(g) selecting developers to be notified in step (d) based on the groups selected in step (c) and step (f).

5. The method of claim 4, further comprising:

(h) analyzing the results of steps (a) through (c) and (e) through (g) to iteratively improve efficacy of the method by updating the method'"'"'s algorithm based on actual results of the method being identified as either false positives or false negatives.

6. The method of claim 1, wherein the proximity score between two tasks is calculated in step (b) based on an actual proximity score with the following weights:

1 if a common file was edited by developers conducting both tasks;

0.59 if a common file was viewed by developers conducting both tasks; and

0.79 if a common file was viewed by a developer conducting one task and edited by a developer conducting the other task.

7. The method of claim 6, wherein the proximity score between two tasks is calculated in step (b) by dividing the actual proximity score by a maximum potential proximity score;

the maximum potential proximity score being the union of files between a task pair that have been edited and/or viewed.

8. The method of claim 1, wherein the threshold in step (c) is a proximity score equal to or greater than the mean+2 standard deviations.

9. The method of claim 1, wherein the selection in step (c) is based on a machine learning algorithm.