CN111930609B - SaaS service software quality evaluation method - Google Patents
SaaS service software quality evaluation method Download PDFInfo
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- CN111930609B CN111930609B CN202010638558.8A CN202010638558A CN111930609B CN 111930609 B CN111930609 B CN 111930609B CN 202010638558 A CN202010638558 A CN 202010638558A CN 111930609 B CN111930609 B CN 111930609B
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Abstract
The invention discloses a quality evaluation method of SaaS service software, which comprises Step1, obtaining a plurality of evaluation data of the quality of the delivered SaaS service software based on a Beohm model; step2, calculating a first correlation coefficient of the post element and the pre element; step3, calculating a second correlation coefficient of the post element and the pre element; step4, establishing an evaluation model; step5, obtaining test values of all last elements of the SaaS service software to be evaluated based on the Beohm model, and evaluating each last element; and Step6, substituting the evaluation result of the last element obtained in Step5 into the evaluation model constructed in Step4 to calculate the probability that the SaaS service software to be evaluated meets the customer requirements, and if the calculation result is greater than a preset threshold value, outputting the evaluation result of the SaaS service software to be evaluated as meeting the customer requirements. The invention can judge whether the software quality can meet the customer requirements at the design completion position of the SaaS service software.
Description
Technical Field
The invention belongs to the technical field of cloud computing, and particularly relates to a quality evaluation method for SaaS service software.
Background
Software-as-a-service (SaaS) is a common cloud computing delivery model for the internet of things at present.
The SaaS operator and the user carry out negotiation about the Service Level Agreement (SLA), and commit the service level, and the user selects different operators according to the quality of service (QoS).
Chinese invention patent CN 201610494567.8 is a method and apparatus for evaluating SaaS service quality by determining a quality of service attribute required by a user, a quality requirement value of the user for each quality of service attribute, and a quality actual value provided by each SaaS operator for each quality of service attribute; normalizing the quality actual value based on the quality required value and the quality actual value to obtain a quality standard value provided by each SaaS operator for each service quality attribute; and calculating a quality comprehensive value of each SaaS operator based on a quality standard value provided by each SaaS operator for each service quality attribute, and selecting the optimal SaaS operator based on the quality comprehensive value. Therefore, while considering user requirements, the accuracy of calculating the comprehensive service quality is ensured through the normalization processing of the service quality which can be provided by the SaaS operator, and the evaluation of the SaaS service quality of different SaaS operators is effectively realized.
However, the evaluation of the SaaS service quality of one SaaS operator is not sufficient, and in addition to the evaluation of the SaaS service quality, the quality of the SaaS service software itself needs to be evaluated.
As shown in FIG. 1, the software quality includes three dimensions of portability, usability and maintainability according to the Boehm model.
Wherein, the influence factors of the portability comprise the independence and the integrity of the equipment;
in addition, the usability is divided into three sub-dimensions of reliability, efficiency and environmental engineering, wherein the influence factors of the reliability comprise integrity, accuracy and consistency; the influencing factors of the efficiency include the efficiency and accessibility of the equipment; environmental engineering influences include accessibility, communication, and structural;
the maintainability is divided into three dimensions of testability, understandability and amendable and modifiable, wherein the influence factors of the testability comprise accessibility, structurability, self-descriptiveness and simplicity; the influence factors of understandability comprise structural property, self-descriptiveness, simplicity and readability; factors influencing the modifiability include structure and expandability.
In practice, when software is developed according to customer requirements, only a few fuzzy descriptions are made on the requirements of customers on the software quality, for example, if the customers only say that the software is good and need to run stably for several years, most of the customers of the software quality, especially for SaaS service software, do not move software knowledge and cannot provide specific requirements on the software quality, and only after the customers use, rough evaluations on the portability, usability, maintainability and the like of the software are abstracted, so that the customers can reflect problems after the software is delivered to the customers for use, and the SaaS operators modify the software for a long time, thereby resulting in poor customer experience.
If the method for evaluating the quality of the SaaS service software is provided, the software design completion part can judge whether the quality requirement of a client is met, so that software designers can be helped to modify the software in time, the software rework times after the client is delivered in the later period are reduced, the workload of the software designers is greatly reduced, and the client experience is improved.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for evaluating the quality of SaaS service software, which can determine whether the software quality can meet the customer requirements at the site where the SaaS service software is designed.
In order to solve the technical problems, the invention adopts the technical scheme that: a quality evaluation method for SaaS service software comprises the following steps:
step1, obtaining evaluation data of the quality of a plurality of delivered and used SaaS service software based on a Beohm model, wherein the evaluation data comprises evaluation results of each element of the Beohm model, and the evaluation results are that customer requirements are met and customer requirements are not met; the test value of each element finally arranged in the Beohm model is within the design value range, the evaluation result is that the customer requirement is met, otherwise, the evaluation result is that the customer requirement is not met;
step2, calculating a first correlation coefficient inh of the post element and the pre element;
the total capacity in the Beohm model is a front element with three dimensions of portability, usability and maintainability;
portability is a pre-element of device independence and integrity;
the workability is a prepositive element of three sub-dimensions of reliability, efficiency and environmental engineering; the reliability is a preposed element of completeness, accuracy and consistency; efficiency is a pre-element of device efficiency and accessibility; environmental engineering is a front element of accessibility, communication and structure;
the maintainability is a preposed element with testability, understandability and modifiable three dimensions; the testability is a prepositive element of accessibility, structure, self-descriptiveness and conciseness; the intelligibility is a preposed element with the characteristics of structuredness, self-descriptiveness, conciseness and readability; a pre-element modifiable to structural and extensible;
wherein the device independence, completeness, accuracy, consistency, device efficiency, accessibility, communication, structural, self-descriptive, terse, legibility, and extensibility are the last-placed elements;
A 1 、A 2 、A 3 ...A n-1 、A n the evaluation results of all direct post-elements under the same pre-element are referred to; inh = P (-B | A) 1 ∩A 2 ∩A 3 ∩...A n-1 ∩A n ) Inh is more than or equal to 0 and less than or equal to 1, and the formula refers to the probability that the preposed element B does not meet the requirements of the clients under the condition that all postpositional elements under the preposed element B meet the requirements of the clients;
step3, calculating a second correlation coefficient p of the post element and the pre element i ；
Said p is i Refers to a postposition element A i The situation that the customer requirements are not met, and a post-element A i The probability that the associated front-end element B also does not meet the customer requirements, i.e. p i ＝P(-B|-A i )；0≤p i Less than or equal to 1; to the postposition element A 1 、A 2 、A 3 ...A n-1 、A n Respectively calculate corresponding p 1 、p 2 、p 3 ...p n-1 、p n ；
Step4, establishing an evaluation model: building models
The above-mentioned
In (b) i The value of (A) is determined by a post-element A of the SaaS service software to be evaluated i When the element A is placed behind, is determined by the current evaluation result of i B when the current evaluation result of (2) is "satisfy customer requirement i =1; when the element A is placed at the back i If the current evaluation result of (b) is "not satisfying the customer request", then b i ＝1-p i 。
Step5, obtaining test values of all last elements of the SaaS service software to be evaluated based on the Beohm model, and evaluating each last element; if the test value is in the design range, the evaluation result is that the customer requirements are met, otherwise, the evaluation result is that the customer requirements are not met;
and Step6, substituting the evaluation result of the last element obtained in Step5 into the evaluation model constructed in Step4 to calculate the probability that the SaaS service software to be evaluated meets the customer requirements, and if the calculation result is greater than a preset threshold value, outputting the evaluation result of the SaaS service software to be evaluated as meeting the customer requirements.
Compared with the prior art, the invention has the following advantages: the software design completion part can judge whether the requirements of customers on the quality are met, so that software designers can be helped to modify the software in time, the software reworking times after the customers are delivered in the later period are reduced, the workload of the software designers is greatly reduced, and the customer experience is improved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a structural diagram of the Beohm model.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1 and 2, a method for evaluating the quality of SaaS service software includes the following steps:
step1, obtaining evaluation data of the quality of a plurality of delivered and used SaaS service software based on a Beohm model, wherein the evaluation data comprises evaluation results of each element of the Beohm model, and the evaluation results are that customer requirements are met and customer requirements are not met; the test value of each element finally arranged in the Beohm model is within the design value range, the evaluation result is that the customer requirement is met, otherwise, the evaluation result is that the customer requirement is not met;
it should be noted that the evaluation results of other elements except the last element in the Beohm model are obtained from the clients, where the clients include the user of the SaaS service software, the consignor of the SaaS service software, the tester of the SaaS service software, and the like;
step2, calculating a first correlation coefficient inh of the post element and the pre element;
the total capacity in the Beohm model is a front element with three dimensions of portability, usability and maintainability;
portability is a pre-element of device independence and integrity;
the workability is a prepositive element of three sub-dimensions of reliability, efficiency and environmental engineering; the reliability is a preposed element of completeness, accuracy and consistency; efficiency is a pre-element of device efficiency and accessibility; environmental engineering is a front element of accessibility, communications, and structural;
the maintainability is a preposed element with testability, understandability and modifiable three dimensions; the testability is a prepositive element of accessibility, structure, self-descriptiveness and conciseness; the intelligibility is a preposed element with the characteristics of structuredness, self-descriptiveness, conciseness and readability; a pre-element that can be modified to be structural and extensible;
wherein the device independence, completeness, accuracy, consistency, device efficiency, accessibility, communication, structural, self-descriptive, terse, legibility, and extensibility are the last-placed elements;
A 1 、A 2 、A 3 ...A n-1 、A n the evaluation results of all direct postelements under the same preposed element are referred to; inh = P (-B | A) 1 ∩A 2 ∩A 3 ∩...A n-1 ∩A n ) Inh is more than or equal to 0 and less than or equal to 1, and the formula refers to the probability that the preposed element B does not meet the requirements of the clients under the condition that all postpositional elements under the preposed element B meet the requirements of the clients;
step3, calculating a second correlation coefficient p of the post element and the pre element i ；
Said p is i Refers to a rear element A i The situation that the customer requirement is not met, and a post-element A i Probability that the associated antecedent element B also does not satisfy the customer requirements, i.e. p i ＝P(-B|-A i )；0≤p i Less than or equal to 1; for the postposition element A 1 、A 2 、A 3 ...A n-1 、A n Respectively calculate corresponding p 1 、p 2 、p 3 ...p n-1 、p n ；
Step4, establishing an evaluation model: building models
The above-mentioned
In (b) i The value of (A) is defined by a post-element A of the SaaS service software to be evaluated i When the current evaluation result of element A is determined i B when the current evaluation result of (2) is "satisfy customer requirement i =1; when the element A is placed at the back i If the current evaluation result of (b) is "not satisfying the customer request", then b i ＝1-p i 。
Step5, obtaining test values of all last elements of the SaaS service software to be evaluated based on the Beohm model, and evaluating each last element; if the test value is in the design range, the evaluation result is that the customer requirements are met, otherwise, the evaluation result is that the customer requirements are not met;
and Step6, substituting the evaluation result of the last element obtained in Step5 into the evaluation model constructed in Step4 to calculate the probability that the SaaS service software to be evaluated meets the customer requirements, and if the calculation result is greater than a preset threshold value, outputting the evaluation result of the SaaS service software to be evaluated as meeting the customer requirements.
The preset threshold value is 0.5-1. Preferably 0.9.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiment according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (1)
1. A quality evaluation method for SaaS service software is characterized by comprising the following steps:
step1, obtaining evaluation data of the quality of a plurality of delivered and used SaaS service software based on a Boehm model, wherein the evaluation data comprises an evaluation result of each element of the Boehm model, and the evaluation result is that the client requirement is met and the client requirement is not met; the test value of each element finally placed in the Boehm model is within the design value range, the evaluation result is that the customer requirement is met, otherwise, the evaluation result is that the customer requirement is not met;
step2, calculating a first correlation coefficient inh of the post element and the pre element;
the total capacity in the Boehm model is a preposed element with three dimensions of portability, usability and maintainability;
portability is a pre-element of device independence and integrity;
the workability is a prepositive element of three sub-dimensions of reliability, efficiency and environmental engineering; the reliability is a preposed element of completeness, accuracy and consistency; efficiency is a pre-element of device efficiency and accessibility; environmental engineering is a front element of accessibility, communications, and structural;
the maintainability is a preposed element with testability, understandability and three dimensions which can be modified; the testability is a prepositive element of accessibility, structure, self-descriptiveness and conciseness; the intelligibility is a preposed element with the characteristics of structuredness, self-descriptiveness, conciseness and readability; a pre-element that can be modified to be structural and extensible;
wherein the device independence, completeness, accuracy, consistency, device efficiency, accessibility, communication, structural, self-descriptive, terse, legibility, and extensibility are the last-placed elements;
A 1 、A 2 、A 3 ...A n-1 、A n the evaluation results of all direct postelements under the same preposed element are referred to; inh = P (-B | A) 1 ∩A 2 ∩A 3 ∩...A n-1 ∩A n ) Inh is more than or equal to 0 and less than or equal to 1, and the formula indicates that all the postelements under the preposed element B meet the requirements of customersNext, the leading element B is the probability of the case that the customer requirements are not met;
step3, calculating a second correlation coefficient p of the post element and the pre element i ；
Said p is i Refers to a postposition element A i The situation that the customer requirement is not met, and a post-element A i The probability that the associated front-end element B also does not meet the customer requirements, i.e. p i ＝P(-B|-A i )；0≤p i Less than or equal to 1; for the postposition element A 1 、A 2 、A 3 ...A n-1 、A n Respectively calculate corresponding p 1 、p 2 、p 3 ...p n-1 、p n ；
Step4, establishing an evaluation model: building models
The described
In (b) i The value of (A) is defined by a post-element A of the SaaS service software to be evaluated i When the current evaluation result of element A is determined i B when the current evaluation result is 'meeting the customer' requirement i =1; when the element A is placed at the back i If the current evaluation result of (1) is "not satisfying the customer request", then b i ＝1-p i ；
Step5, obtaining test values of all last elements of the SaaS service software to be evaluated based on the Beohm model, and evaluating each last element; if the test value is in the design range, the evaluation result is that the customer requirements are met, otherwise, the evaluation result is that the customer requirements are not met;
and Step6, substituting the evaluation result of the last element obtained in Step5 into the evaluation model constructed in Step4 to calculate the probability that the SaaS service software to be evaluated meets the customer requirements, and if the calculation result is greater than a preset threshold value, outputting the evaluation result of the SaaS service software to be evaluated as meeting the customer requirements.
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| CN107644292A (en) * | 2017-09-08 | 2018-01-30 | 北京寄云鼎城科技有限公司 | SaaS service quality guarantee method and devices |
| CN107786651A (en) * | 2017-10-23 | 2018-03-09 | 云南大学 | SaaS service Evolution consistency decision methods with tenant's evolution tolerance |
| CN110659213A (en) * | 2019-09-24 | 2020-01-07 | 郑州航空工业管理学院 | Software quality evaluation method based on intuition fuzziness |
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