COMPUTER-IMPLEMENTED METHOD FOR DETERMINING AT LEAST ONE QUALITY ATTRIBUTE FOR AT LEAST ONE DEFECT OF INTEREST

Abstract

Provided is a computer-implemented method for determining at least one quality attribute for at least one defect of interest, including the steps: a. providing an input data set including the at least one defect of interest; b. determining the at least one quality attribute for the at least one defect of interest using a classification algorithm based on the input data set; and c. providing the determined at least one quality attribute and/or additional output information as output. Further, a computing unit and a computer program product are provided.

Claims

1. A computer-implemented method for determining at least one quality attribute for at least one defect of interest, comprising the steps: a. providing an input data set comprising the at least one defect of interest; b. determining the at least one quality attribute for the at least one defect of interest using a classification algorithm based on the input data set; and c. providing the determined at least one quality attribute and/or additional output information as output.

2. The computer-implemented method according to claim 1, wherein the at least one quality attribute corresponds to the ISO 25010 standard.

3. The computer-implemented method according to claim 1, wherein the input data set is defined as defect description, in textual format and/or stored in a computer-readable volatile or non-volatile storage medium.

4. The computer-implemented method according to claim 3, wherein the machine learning algorithm is a machine learning algorithm or rule-based.

5. The computer-implemented method according to claim 1, wherein the method further comprises the step initiating at least one action depending on the determined at least one quality attribute and/or related output information, wherein the related output information is information associated with the at least one defect of interest, associated defect information or the at least one determined quality attribute.

6. The computer-implemented method according to claim 5, wherein the at least one action is an action selected form the group comprising: processing the at least one determined quality attribute; prioritizing the at least one defect of interest to be resolved; identifying the need or urgency for fast resolution or urgency; and identifying at least one gap in the quality assurance process.

7. A computing unit for performing the method steps according to claim 1.

8. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method directly loadable into an internal memory of a computer, comprising software code portions for performing the steps according to claim 1 when the computer program product is running on a computer.

Description

BRIEF DESCRIPTION

[0058] Some of the embodiments will be described in detail, with reference to the following figures, wherein like designations denote like members, wherein:

[0059] FIG. 1 shows a schematic diagram of the method according to an embodiment of the invention; and

[0060] FIG. 2 shows a schematic representation of the classification according to an embodiment of the invention.

DETAILED DESCRIPTION

[0061] FIG. 1 illustrates a flowchart of the method according to embodiments of the invention with the method steps S1 to S3.

[0062] In the first step S1 the input data set is received or provided as input for the subsequent method steps S2 to S3. Thereby, the input data set comprises the at least one defect of interest 10. Additionally, the input data can comprise further data, namely associated defect information.

[0063] According to an embodiment, the input data set is provided as defect description, according to which, content of different properties or features of a defect description. This defect description can be provided as textual information like the title, detailed description, comments, as well as numerical information as the number of files changed for a fixed defect.

[0064] In the next method step S2, the at least one quality attribute 20 for the at least one defect of interest 10 is determined by means of a classification algorithm 30 on the basis of the received input data set. Therefore, heuristics and keywords, natural language processing or machine learning can be used for the classification task in step S2.

[0065] In the last method step S3, the determined at least one quality attribute 20 and/or additional output information is outputted or provided as output. This output can be further handled or processed.

[0066] In other words, the method according to embodiments of the invention enhances defect descriptions by classifying them regarding quality attributes based on the provided defect description.

[0067] FIG. 2 shows a schematic representation of the classification according to an embodiment of embodiments of the invention. The defects 10 are fed as input into the classification algorithm 30. After classification of the defects 10, the resulting one or more quality attributes 20 are provided as output. Exemplary quality attributes are listed in FIG. 2. According to an embodiment, the resulting quality attribute meets the ISO standard 25010.

[0068] Use Case 1 [0069] Context: Software for grid control (distribution of energy), e.g., monitoring, analyzing and preventing outages, planning changes of the system configuration [0070] Input: defect (software malfunction) [0071] Output: classification of the defect corresponds to the quality, which is affected, e.g. a defect which results in a system crash affects the reliability of the software: defect=crash/class=reliability [0072] Action: class reliability has highest priority=>in consequence the defect has highest priority [0073] Benefit: high priority defects can be detected and fixed fast which increases the overall quality of the product and the customer satisfaction

[0074] Use Case 2 [0075] Context: Software for grid control (distribution of energy), e.g., monitoring, analyzing and preventing outages, planning changes of the system configuration [0076] Input: set of defects (software malfunction) [0077] Output: classification of all defects in the set corresponding to the qualities which are affected, identifying e.g., high percentage of defects affecting the same class, like security [0078] Action: architectural measures to address the root cause of the defects in the design of the software [0079] Benefit: minimize risk of further defects related to this quality, in the example security

[0080] Although the present invention has been disclosed in the form of preferred embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.

[0081] For the sake of clarity, it is to be understood that the use of “a” or “an” throughout this application does not exclude a plurality, and “comprising” does not exclude other steps or elements.

REFERENCE SIGNS

[0082] 10 defect of interest [0083] 30 classification algorithm [0084] 20 quality attribute [0085] S1 to S3 Method steps 1 to 3