CN113760705A - Software quality testing method and device, electronic equipment and storage medium - Google Patents
Software quality testing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a software quality testing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring user behavior information of software to be evaluated, and determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period; determining a software quality test model matched with the software to be evaluated according to the operation life cycle; and testing the software quality of the software to be evaluated according to the software quality test model. According to the technical scheme of the embodiment of the invention, the software quality testing model corresponding to the operation life cycle of the software to be evaluated is matched for the software to be evaluated, so that the problem that the software quality testing precision of the software to be evaluated is difficult to guarantee is solved, and the effect of high-precision software quality testing at each stage in the operation process of the software to be evaluated is realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of information processing, in particular to a software quality testing method and device, electronic equipment and a storage medium.
Background
With the continuous development of modern engineering technology, software becomes an important independent branch, and effective guarantee of software quality is an important prerequisite for software application. With the increase of the importance and complexity of software in the e-commerce industry, the requirements on the reliability and safety of the software are continuously increased, so that the effective determination of the software quality becomes an important means for ensuring the software quality.
The existing software quality testing scheme mainly takes an attribute-oriented object as a main object, and tests the software quality by reconstructing or existing software quality testing indexes and combining a software quality testing model in the software testing and software using processes.
In the process of implementing the invention, the inventor finds that the following technical problems exist in the prior art: the accuracy of the software quality determined based on the existing software quality test scheme is difficult to guarantee.
Disclosure of Invention
The embodiment of the invention provides a software quality testing method and device, electronic equipment and a storage medium, so as to realize the effect of accurately testing the software quality of software to be evaluated.
In a first aspect, an embodiment of the present invention provides a software quality testing method, which may include:
acquiring user behavior information of software to be evaluated, and determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period;
determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and testing the software quality of the software to be evaluated according to the software quality test model.
Optionally, determining the operation life cycle of the software to be evaluated according to the user behavior information may include:
determining a user characteristic vector according to the user behavior information, and inputting the user characteristic vector into an operation life cycle determination model which is trained in advance; and determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model.
Optionally, determining the user feature vector according to the user behavior information may include:
determining a user characteristic sub-vector according to the user behavior information, wherein the user characteristic sub-vector comprises a user quantity characteristic sub-vector, a software visit quantity characteristic sub-vector and/or a software visit quantity characteristic sub-vector; and combining the sub-vectors of the user features to obtain the user feature vector.
Optionally, the combining the user feature sub-vectors to obtain the user feature vector may include:
connecting the sub-vectors of the user features end to generate a joint vector, and performing feature extraction on the joint vector to obtain a user feature vector; or combining the sub-vectors of the user features based on the complex vector, and extracting the features in the complex vector space to obtain the user feature vector.
Optionally, determining the user feature sub-vector according to the user behavior information may include:
determining a user quantity characteristic value according to the user behavior information, and determining a user quantity characteristic sub-vector according to the user quantity characteristic value, wherein the user quantity characteristic value comprises an active user quantity ring ratio, a newly-added user quantity ring ratio, a lost user quantity ring ratio and/or the number of software users; and/or determining a software access quantity characteristic value according to the user behavior information, and determining a software access quantity characteristic sub-vector according to the software access quantity characteristic value, wherein the software access quantity characteristic value comprises a page access quantity and/or a page access quantity ring ratio; and/or; and determining a software visitor volume characteristic value according to the user behavior information, and determining a software visitor volume characteristic sub-vector according to the software visitor volume characteristic value, wherein the software visitor volume characteristic value comprises an independent visitor number and/or an independent visitor number ring ratio.
Optionally, the operation lifecycle determination model may be trained by:
acquiring a historical feature vector and a historical life cycle of historical evaluation software, and taking the historical feature vector and the historical life cycle as a group of training samples; and training the original neural network model based on a plurality of groups of training samples to obtain an operation life cycle determination model.
Optionally, when the operation life cycle is a seed period, the software quality test model is a model constructed based on a weighted summation method; and/or when the operation life cycle is a platform period, the software quality test model is a model constructed based on a Cartesian product method; and/or when the operation life cycle is an outbreak period, the software quality test model is constructed based on a mixing method, and the mixing method comprises a Cartesian product method and a weighted summation method.
In a second aspect, an embodiment of the present invention further provides a software quality testing apparatus, where the apparatus may include:
the operation life cycle determining module is used for acquiring user behavior information of the software to be evaluated and determining the operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period;
the software quality test model matching module is used for determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and the software quality testing module is used for testing the software quality of the software to be evaluated according to the software quality testing model.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device may include:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the software quality testing method provided by any of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the software quality testing method provided in any embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, the operation life cycle of the software to be evaluated is identified through the user behavior information of the software to be evaluated, so that a software quality test model which is suitable for the software to be evaluated at the stage in the operation process is matched for the software to be evaluated according to the operation life cycle; and further testing the software quality of the software to be evaluated according to the software quality testing model. According to the technical scheme, the software quality testing model which is suitable for the stage of the software to be evaluated is matched for the software to be evaluated, so that the problem that the software quality testing precision of the software to be evaluated is difficult to guarantee is solved, and the effect of high-precision testing of the software quality of the software to be evaluated at each stage in the operation process is achieved.
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FIG. 1 is a flowchart of a software quality testing method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a software quality testing method according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a software quality testing method according to a third embodiment of the present invention;
FIG. 4 is a diagram of an alternative software quality testing method according to a third embodiment of the present invention;
fig. 5 is a block diagram of a software quality testing apparatus according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device in a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before the embodiment of the present invention is described, an application scenario of the embodiment of the present invention is exemplarily described: when the software to be evaluated is in different operation life cycles, the software quality of the software to be evaluated is determined based on the same software quality test model in the prior art, which ignores that the emphasis points of the development quality of the software to be evaluated in different operation life cycles are different, which directly affects the test precision of the software quality.
In order to solve the problem, embodiments of the present invention provide that an operation lifecycle of software to be evaluated is identified, where the operation lifecycle may be a lifecycle of the software to be evaluated in an operation process, and then a software quality test model matched with the software to be evaluated is obtained according to the operation lifecycle, that is, the software quality test model may change correspondingly according to a change of the operation lifecycle of the software to be evaluated, so as to accurately test software quality of the software to be evaluated according to the matched software quality test model.
In practical application, optionally, the operation lifecycle may include a seed period, an outbreak period, a platform period, and the like, where the seed period may be understood as a software development period, developers usually develop a model with a simpler function for a small number of specific users to try out, and the software quality of the software to be evaluated in the seed period is more in consideration of a fast iteration product, and the function of the software is increased by fast iteration; the outbreak period can be understood as a user quantity rapid growth stage, a specific user oriented to a user is converted into a public user, and more software quality of software to be evaluated in the outbreak period is to consider rapid quality problem discovery and rapid iterative function increase; the platform period can be understood as a user quantity stabilization stage, the user quantity cannot rapidly increase at this time, and more software quality of software to be evaluated in the platform period is considered to rapidly find a quality problem; and so on.
Example one
Fig. 1 is a flowchart of a software quality testing method according to an embodiment of the present invention. The embodiment can be applied to the condition of testing the software quality of the software to be evaluated, and is particularly suitable for the condition of matching a corresponding software quality test model for the software to be evaluated according to the operation life cycle of the software to be evaluated, and further testing the software quality of the software to be evaluated according to the software quality test model. The method can be executed by the software quality testing device provided by the embodiment of the invention, the device can be realized by software and/or hardware, the device can be integrated on electronic equipment, and the electronic equipment can be a user terminal or a server.
Referring to fig. 1, the method of the embodiment of the present invention specifically includes the following steps:
s110, obtaining user behavior information of the software to be evaluated, and determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period.
The software to be evaluated may be application software to be evaluated, user behavior information of the software to be evaluated may be obtained from each big data platform, for example, the user behavior information of the software to be evaluated may be extracted from a big data platform such as a rights platform and a data acquisition platform according to a code (appCode) of the software to be evaluated, the user behavior information may be behavior information of which user logs in the software to be evaluated when, what operation the user performs in the software to be evaluated, and the like, and the behavior information may also be understood as track information.
Further, determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle has a plurality of determination schemes, for example, obtaining a user characteristic vector which is pre-constructed and is related to the operation life cycle, determining a specific numerical value of the user characteristic vector according to the user behavior information, and processing the specific numerical value to obtain the operation life cycle of the software to be evaluated; inputting the user behavior information into an operation life cycle determination model which is trained in advance, and obtaining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model; and so on.
And S120, determining a software quality test model matched with the software to be evaluated according to the operation life cycle.
After the operation life cycle of the software to be evaluated is identified, a software quality test model corresponding to the stage of the software to be evaluated in the operation process can be matched for the software to be evaluated according to the operation life cycle, for example, a suitable software quality test model suitable for the software to be evaluated in different operation life cycles is matched for the software to be evaluated based on a preset rule, so that the software quality test model is suitable for actual requirements and accords with the characteristics of a software development cycle. On the basis, for example, the preset rule may be that when the operation lifecycle is a seed period, the software quality test model may be a model constructed based on a weighted summation method; when the operation life cycle is a platform period, the software quality test model can be a model constructed based on a Cartesian product method; when the operation life cycle is an outbreak period, the software quality test model can be a model constructed based on a hybrid method, including a cartesian product method and a weighted summation method.
And S130, testing the software quality of the software to be evaluated according to the software quality test model.
The software quality of the software to be evaluated can be tested according to the software quality test model, and the software quality can be presented in various forms, such as scores, grades and the like.
Based on the above, for example, the model constructed based on the weighted summation method can calculate the score of the software to be evaluated in terms of software quality based on the following formula, where S is the comprehensive score of the software quality, βiIs the weight coefficient of the ith quality indicator, XiThe quality indexes can be corresponding indexes determined by combining ISO-9126 or ISO/IEC25010:2011 software quality models according to software characteristics of different industries, such as process quality, result quality, effective BUG rate, BUG timely solving rate, test coverage rate, online success rate and the like, so that the determination result of the software quality test model constructed based on the quality indexes is more credible, wherein ISO is the abbreviation of the International Organization for Standardization.
Illustratively, the model constructed based on the Cartesian product method can calculate the score of the software to be evaluated in terms of software quality based on the following formula, wherein S is the comprehensive score of the software quality,ithe index score of the ith quality index is the number of the quality indexes.
SX1×…Xi…×Xn
For another example, the model constructed based on the hybrid method may calculate the score of the software to be evaluated in terms of software quality based on the following formula, where S is the comprehensive score of the software quality, βiIs the weight coefficient of the ith quality indicator, XiThe index score of the ith quality index is the number of the quality indexes.
Or the light source is used for emitting light,
according to the technical scheme of the embodiment of the invention, the operation life cycle of the software to be evaluated is identified through the user behavior information of the software to be evaluated, so that a software quality test model which is suitable for the software to be evaluated at the stage in the operation process is matched for the software to be evaluated according to the operation life cycle; and further testing the software quality of the software to be evaluated according to the software quality testing model. According to the technical scheme, the software quality testing model which is suitable for the stage of the software to be evaluated is matched for the software to be evaluated, so that the problem that the software quality testing precision of the software to be evaluated is difficult to guarantee is solved, and the effect of high-precision testing of the software quality of the software to be evaluated at each stage in the operation process is achieved.
Example two
Fig. 2 is a flowchart of a software quality testing method according to a second embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. In this embodiment, optionally, determining the operation lifecycle of the software to be evaluated according to the user behavior information may specifically include: determining a user characteristic vector according to the user behavior information, and inputting the user characteristic vector into an operation life cycle determination model which is trained in advance; and determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
Referring to fig. 2, the method of the present embodiment may specifically include the following steps:
and S210, acquiring user behavior information of the software to be evaluated.
S220, determining a user characteristic vector according to the user behavior information, inputting the user characteristic vector into a pre-trained operation life cycle determination model, and determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period.
The user feature vector may be a feature vector related to an operation lifecycle, such as a feature vector related to a user amount, a software visit amount, and the like, and the user feature vector may be determined according to user behavior information, for example, performing classification statistics on the user behavior information, and determining a corresponding user feature vector according to statistical results under different classifications.
The operation life cycle determination model is a model for identifying the operation life cycle in which the software to be evaluated is located according to the user feature Vector, and may be a supervised, semi-supervised, unsupervised classifier model, such as a K-nearest neighbor method, a Support Vector Machine (SVM), or a learning algorithm of the rest of the neural network. Therefore, after the user feature vector is input to the operation life cycle determination model trained in advance as input data, the operation life cycle of the software to be evaluated can be obtained according to the output result of the operation life cycle determination model.
And S230, determining a software quality test model matched with the software to be evaluated according to the operation life cycle, and testing the software quality of the software to be evaluated according to the software quality test model.
According to the technical scheme of the embodiment of the invention, the operation life cycle of the software to be evaluated can be accurately identified by inputting the user characteristic vector determined according to the user behavior information into the operation life cycle determination model which is trained in advance.
On this basis, an optional technical solution is that the operation lifecycle determination model may be trained in the following manner: acquiring historical feature vectors and historical life cycles of historical evaluation software, and taking the historical feature vectors and the historical life cycles as a group of training samples, wherein the historical life cycles can be determined by artificial labeling of each expert; and training the original neural network model based on a plurality of groups of training samples to obtain an operation life cycle determination model.
To better understand the model training process described above, it is exemplified below with reference to specific examples. Illustratively, taking SVM as an example, each software to be evaluated is divided into a training sample set R and a testing sample set T, where R ═ { R ═ R1,r2,…,reE represents the number of software to be evaluated in the training sample set, T ═ T1,t2,…,tnN represents the number of software to be evaluated in the test sample. Respectively extracting user quantity feature sub-vectors G from RjSoftware access vector feature sub-vector PjAnd software visitor volume feature subvector QjAnd constructing a user feature vector H by adopting a serial combination mode, wherein H is { H ═ H }1,H2,…,Hj…,HeJ ranges from 0 to e. Extracting H from R and corresponding class label H of RlabelAs input data for the SVM, with SVM as output data, where Hlabel＝{label1,label2,…,labelj,…,labele}, and labeljThe value range of (1) to (m) is 1 to (3), which respectively represent a seed period, an explosion period and a plateau period,labeljis obtained by the artificial marking of an expert. The trained SVM is used for identifying the operation life cycle of the software to be identified in the T, and the identification result can be measured through the identification accuracy rate D, wherein D is D/n, D represents the correct identification number in the T, and n represents the number of the software to be evaluated in the T.
EXAMPLE III
Fig. 3 is a flowchart of a software quality testing method provided in the third embodiment of the present invention. The present embodiment is optimized based on the technical solutions in the second embodiment. In this embodiment, optionally, determining the user feature vector according to the user behavior information may specifically include: determining a user characteristic sub-vector according to the user behavior information, wherein the user characteristic sub-vector comprises a user quantity characteristic sub-vector, a software visit quantity characteristic sub-vector and/or a software visit quantity characteristic sub-vector; and combining the sub-vectors of the user features to obtain the user feature vector. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
Referring to fig. 3, the method of this embodiment may specifically include the following steps:
s310, obtaining user behavior information of software to be evaluated, and determining a user characteristic sub-vector according to the user behavior information, wherein the user characteristic sub-vector comprises a user quantity characteristic sub-vector, a software visit quantity characteristic sub-vector and/or a software visit quantity characteristic sub-vector.
The user feature sub-vector may be a feature sub-vector related to the operation lifecycle, such as a user quantity feature sub-vector, a software access quantity feature sub-vector, and the like. When the user behavior information of the software to be evaluated is acquired, corresponding user characteristic sub-vectors can be calculated in real time according to the user behavior information, and particularly, each user characteristic sub-vector can be determined by adopting a corresponding scheme.
Exemplarily, a user quantity characteristic value is determined according to the user behavior information, and then a user quantity characteristic sub-vector can be determined according to the user quantity characteristic value, wherein the user quantity characteristic value can be an active user quantity ring ratio, a newly added user quantity ring ratio, a streamLoss of user quantum ring ratio, number of software users, etc. It should be noted that the software user number usersum may be the number of users who have opened the use permission of the software to be evaluated, or the number of users who have registered the software to be evaluated; the number of active users may be the number of users whose contribution values to Page View (PV) and independent visitor number (UV) are within a certain threshold range in a fetching period, the fetching period may be a preset period for periodically acquiring user behavior information, such as every week, every month, every quarter, and the like, and correspondingly, the active user number ring ratio may be (the number of active users in the current fetching period-the number of active users in the previous fetching period)/the number of active users in the previous fetching period, that is, the number of users in the current fetching period
n is the current access cycle, and n-1 is the previous access cycle; the number of newly added users can be the number of users of the new opening authority of the software to be evaluated in the access period, and the ring ratio of the number of newly added users can be (the number of newly added users in the current access period-the number of newly added users in the previous access period)/the number of newly added users in the previous access period, namely
The number of lost users may be the number of users who newly cancel the right of the software to be evaluated in the access period, and the ring ratio of the number of lost users may be (the number of lost users in the current access period-the number of lost users in the previous access period)/the number of lost users in the previous access period, that is, the number of lost users in the current access period
On this basis, optionally, the user quantity feature sub-vector determined according to the user quantity feature value may be represented as G ═ usersum, active, newrate, lostrate }.
Exemplarily, the characteristic value of the software access quantity is determined according to the user behavior information, and then the characteristic sub-vector of the software access quantity is determined according to the characteristic value of the software access quantity, wherein the characteristic value of the software access quantity can beIncluding page access volume, page access volume ring ratio, etc. It should be noted that the page access amount (PV) is the sum of the number of times that the page where the software to be evaluated is located is browsed in the access period, that is, pvsum ∑ PV, since the data in the big data platform may be data statistics in units of hour, day, and week, and the access period may be data acquisition in units of day, week, and month, the calculation formula of PV involves summation operation; accordingly, the page-access quantum ring ratio may be (PV in the current fetch cycle-PV in the previous fetch cycle)/PV in the previous fetch cycle, i.e.
On this basis, the software access quantity feature subvector determined from the software access quantity feature value can be expressed as P ═ pvsum, pvrate }.
By further example, a software visitor volume characteristic value is determined according to the user behavior information, and a software visitor volume characteristic sub-vector is determined according to the software visitor volume characteristic value, wherein the software visitor volume characteristic value may include an independent visitor number, an independent visitor number ring ratio and the like. It should be noted that the independent visitor number (UV) may be the sum of the numbers of visitors identified by the software to be evaluated according to the browser Cookie in the fetching cycle, that is, uvsum ∑ UV; and the independent visitor number ring ratio may be (UV in current fetch cycle-UV in previous fetch cycle)/UV in previous fetch cycle, i.e. UV in previous fetch cycle
On this basis, the software visitor volume feature subvector determined according to the software visitor volume feature value may be Q ═ uvsum, uvrate }.
And S320, combining the sub-vectors of the user features to obtain the user feature vector.
The combination scheme of each user characteristic sub-vector can have various implementation modes, such as serial combination, wherein each user characteristic sub-vector is connected end to generate a joint vector, the joint vector is subjected to characteristic extraction to obtain a user characteristic vector, namely, any two groups of user characteristic sub-vectors are connected end to generate a joint vector, and the joint vector is used as a new characteristic vector so as to perform characteristic extraction in a higher-dimensional vector space to obtain a user characteristic vector; for example, in the parallel combination, each user feature sub-vector is combined based on the complex vector, and feature extraction is performed in the complex vector space to obtain a user feature vector, that is, two groups of feature vectors are combined together by using the complex vector, and feature extraction is performed in the complex vector space to obtain a user feature vector; of course, other combinations are also possible, and are not specifically limited herein. For example, the user feature vector H is constructed in a serial combination manner, and assuming that H can be composed of G, P and Q, H is { G, P, Q }.
S330, inputting the user characteristic vector into a pre-trained operation life cycle determination model, and determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period.
And S340, determining a software quality test model matched with the software to be evaluated according to the operation life cycle, and testing the software quality of the software to be evaluated according to the software quality test model.
According to the technical scheme of the embodiment of the invention, the user characteristic sub-vectors are determined through the user behavior information, and the user characteristic sub-vectors are combined to obtain the user characteristic vector, so that the effect of accurately constructing the user characteristic vector according to the user behavior information is achieved.
In order to better understand the specific implementation process of the above steps, the following describes an exemplary software quality testing method according to this embodiment with reference to specific examples. Exemplarily, as shown in fig. 4, user behavior information of software to be evaluated is obtained, a user quantity characteristic value, a software access quantity characteristic value and a software access quantity characteristic value are calculated according to the user behavior information, and feature extraction is performed on each characteristic value to construct a feature vector; inputting the feature vector into a supervised classifier model trained in advance, and identifying the operation life cycle of the software to be evaluated according to the output result of the classifier model; and recommending a corresponding software quality test model for the software to be evaluated according to the operation life cycle so as to evaluate the software quality of the software to be evaluated according to the software quality test model.
Example four
Fig. 5 is a block diagram of a software quality testing apparatus according to a fourth embodiment of the present invention, which is configured to execute the software quality testing method according to any of the embodiments. The device and the software quality testing method of each embodiment belong to the same inventive concept, and details which are not described in detail in the embodiment of the software quality testing device can refer to the embodiment of the software quality testing method. Referring to fig. 5, the apparatus may specifically include: an operational lifecycle determination module 410, a software quality testing model matching module 420, and a software quality testing module 430.
The operation lifecycle determining module 410 is configured to obtain user behavior information of software to be evaluated, and determine an operation lifecycle of the software to be evaluated according to the user behavior information, where the operation lifecycle includes a seed period, an explosion period, and/or a platform period;
the software quality test model matching module 420 is used for determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and the software quality testing module 430 is configured to test the software quality of the software to be evaluated according to the software quality testing model.
Optionally, the operation lifecycle determining module 410 may specifically include:
the user characteristic vector determining submodule is used for determining a user characteristic vector according to the user behavior information and inputting the user characteristic vector into an operation life cycle determining model which is trained in advance;
and the operation life cycle determining submodule is used for determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determining model.
Optionally, the user feature vector determining sub-module may specifically include:
the user characteristic sub-vector determining unit is used for determining a user characteristic sub-vector according to the user behavior information, wherein the user characteristic sub-vector comprises a user quantity characteristic sub-vector, a software visit quantity characteristic sub-vector and/or a software visit quantity characteristic sub-vector;
and the user characteristic vector determining unit is used for combining the user characteristic sub-vectors to obtain the user characteristic vector.
Optionally, the user feature vector determining unit may be specifically configured to:
connecting the sub-vectors of the user features end to generate a joint vector, and performing feature extraction on the joint vector to obtain a user feature vector; or combining the sub-vectors of the user features based on the complex vector, and extracting the features in the complex vector space to obtain the user feature vector.
Optionally, the user feature sub-vector determining unit may be specifically configured to:
determining a user quantity characteristic value according to the user behavior information, and determining a user quantity characteristic sub-vector according to the user quantity characteristic value, wherein the user quantity characteristic value comprises an active user quantity ring ratio, a newly-added user quantity ring ratio, a lost user quantity ring ratio and/or the number of software users; and/or determining a software access quantity characteristic value according to the user behavior information, and determining a software access quantity characteristic sub-vector according to the software access quantity characteristic value, wherein the software access quantity characteristic value comprises a page access quantity and/or a page access quantity ring ratio; and/or; and determining a software visitor volume characteristic value according to the user behavior information, and determining a software visitor volume characteristic sub-vector according to the software visitor volume characteristic value, wherein the software visitor volume characteristic value comprises an independent visitor number and/or an independent visitor number ring ratio.
Optionally, on this basis, the operating lifecycle determining module may further include:
the training sample obtaining submodule is used for obtaining a historical characteristic vector and a historical life cycle of historical evaluation software and taking the historical characteristic vector and the historical life cycle as a group of training samples;
and the operation life cycle determination model obtaining submodule is used for training the original neural network model based on a plurality of groups of training samples to obtain an operation life cycle determination model.
Optionally, when the operation life cycle is a seed period, the software quality test model is a model constructed based on a weighted summation method; and/or when the operation life cycle is a platform period, the software quality test model is a model constructed based on a Cartesian product method; and/or when the operation life cycle is an outbreak period, the software quality test model is constructed based on a mixing method, and the mixing method comprises a Cartesian product method and a weighted summation method.
According to the software quality testing device provided by the fourth embodiment of the invention, the operation life cycle determining module and the software quality testing model matching module are matched with each other, and the operation life cycle of the software to be evaluated is identified according to the user behavior information of the software to be evaluated, so that a software quality testing model which is suitable for the software to be evaluated in the operation process is matched for the software to be evaluated according to the operation life cycle; and then, the software quality testing module tests the software quality of the software to be evaluated according to the software quality testing model. According to the device, the software quality testing model which is suitable for the stage of the software to be evaluated is matched for the software to be evaluated, so that the problem that the software quality testing precision of the software to be evaluated is difficult to guarantee is solved, and the effect of high-precision testing of the software quality of the software to be evaluated at each stage in the operation process is achieved.
The software quality testing device provided by the embodiment of the invention can execute the software quality testing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the software quality testing apparatus, each unit and each module included in the embodiment are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE five
Fig. 6 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention, as shown in fig. 6, the electronic device includes a memory 510, a processor 520, an input device 530, and an output device 540. The number of the processors 520 in the electronic device may be one or more, and one processor 520 is taken as an example in fig. 6; the memory 510, processor 520, input device 530, and output device 540 in the electronic device may be connected by a bus or other means, such as by bus 550 in fig. 6.
The memory 510 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the software quality testing method in the embodiment of the present invention (for example, the operation lifecycle determining module 410, the software quality testing model matching module 420, and the software quality testing module 430 in the software quality testing apparatus). The processor 520 executes various functional applications and data processing of the electronic device by executing software programs, instructions and modules stored in the memory 510, that is, implements the software quality testing method described above.
The memory 510 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 510 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 510 may further include memory located remotely from processor 520, which may be connected to devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the device. The output device 540 may include a display device such as a display screen.
EXAMPLE six
A sixth embodiment of the present invention provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a software quality testing method, the method including:
acquiring user behavior information of software to be evaluated, and determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period;
determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and testing the software quality of the software to be evaluated according to the software quality test model.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the software quality testing method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. With this understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A software quality testing method is characterized by comprising the following steps:
acquiring user behavior information of software to be evaluated, and determining an operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period;
determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and testing the software quality of the software to be evaluated according to the software quality test model.
2. The method according to claim 1, wherein the determining the operation lifecycle of the software to be evaluated according to the user behavior information comprises:
determining a user characteristic vector according to the user behavior information, and inputting the user characteristic vector into an operation life cycle determination model which is trained in advance;
and determining the operation life cycle of the software to be evaluated according to the output result of the operation life cycle determination model.
3. The method of claim 2, wherein determining a user feature vector according to the user behavior information comprises:
determining a user characteristic sub-vector according to the user behavior information, wherein the user characteristic sub-vector comprises a user quantity characteristic sub-vector, a software visit quantity characteristic sub-vector and/or a software visit quantity characteristic sub-vector;
and combining the sub-vectors of the user features to obtain a user feature vector.
4. The method of claim 3, wherein said combining each of said user feature sub-vectors to obtain a user feature vector comprises:
connecting the sub-vectors of the user features end to generate a joint vector, and extracting the features of the joint vector to obtain a user feature vector; or the light source is used for emitting light,
and combining the sub-vectors of the user features based on the complex vector, and extracting features in a complex vector space to obtain the user feature vector.
5. The method of claim 3, wherein determining a user feature sub-vector according to the user behavior information comprises:
determining a user quantity characteristic value according to the user behavior information, and determining the user quantity characteristic sub-vector according to the user quantity characteristic value, wherein the user quantity characteristic value comprises an active user quantity ring ratio, a newly-added user quantity ring ratio, a lost user quantity ring ratio and/or software user quantity; and/or the presence of a gas in the gas,
determining a software access quantity characteristic value according to the user behavior information, and determining a software access quantity characteristic sub-vector according to the software access quantity characteristic value, wherein the software access quantity characteristic value comprises a page access quantity and/or a page access quantity ring ratio; and/or;
and determining a software visitor volume characteristic value according to the user behavior information, and determining a software visitor volume characteristic sub-vector according to the software visitor volume characteristic value, wherein the software visitor volume characteristic value comprises an independent visitor number and/or an independent visitor number ring ratio.
6. The method of claim 2, wherein the operational lifecycle determination model is trained by:
acquiring a historical feature vector and a historical life cycle of historical evaluation software, and taking the historical feature vector and the historical life cycle as a group of training samples;
and training an original neural network model based on a plurality of groups of training samples to obtain the operation life cycle determination model.
7. The method of claim 1, wherein when the operational lifecycle is the seed period, the software quality test model is a model constructed based on a weighted summation method; and/or the presence of a gas in the gas,
when the operation life cycle is the platform period, the software quality test model is a model constructed based on a Cartesian product method; and/or the presence of a gas in the gas,
and when the operation life cycle is the outbreak period, the software quality test model is a model constructed based on a mixed method, and the mixed method comprises the Cartesian product method and the weighted summation method.
8. A software quality testing apparatus, comprising:
the operation life cycle determining module is used for acquiring user behavior information of software to be evaluated and determining the operation life cycle of the software to be evaluated according to the user behavior information, wherein the operation life cycle comprises a seed period, an outbreak period and/or a platform period;
the software quality test model matching module is used for determining a software quality test model matched with the software to be evaluated according to the operation life cycle;
and the software quality testing module is used for testing the software quality of the software to be evaluated according to the software quality testing model.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the software quality testing method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the software quality testing method according to any one of claims 1 to 7.
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