CN109299381A - A kind of software defect retrieval and analysis system and method based on semantic concept - Google Patents
A kind of software defect retrieval and analysis system and method based on semantic concept Download PDFInfo
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Abstract
The software defect retrieval and analysis system and method that the invention proposes a kind of based on semantic concept, belong to technical field of software engineering.The system and method include that categorization module, semantic concept module and user feedback module and its corresponding step realize software defect retrieval and analysis.The efficiency for improving retrieval and analysis allows developer and user thus to find relevant software defect and suitable solution.
Description
Technical field
The software defect retrieval and analysis system and method that the present invention relates to a kind of based on semantic concept, belong to soft project
Technical field.
Background technique
In recent years, a large amount of softwares are quickly developed.Particularly, larger in order to satisfy social needs, it is more complicated
Software has been designed to come out.Meanwhile many software defects also occur therewith.Sometimes, these defects result in cost and greatly stop
The machine time.It will be apparent that harm caused by software defect is immeasurable, so, it is necessary in software development and test phase
Suitable software defect is retrieved and analyzed.In order to complete to retrieve and analyze the task of related software defect, many softwares
The model and method of defect prospecting and analysis has been suggested.However, they are not high for the accuracy of defect retrieval, use
Defect retrieval developer needs to do more work instead.In addition to this, these model and methods do not account for developer
With the comment of user.
Summary of the invention
The present invention in order to solve it is above-mentioned lack in the prior art the software defect of software development and test phase retrieval and
The technical issues of analysis, proposes a kind of software defect retrieval and analysis system and method based on semantic concept, is taken
Technical solution is as follows:
A kind of software defect retrieval and analysis system based on semantic concept, the system comprises:
Categorization module for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept module of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback module of user's information；
The categorization module includes:
Software defect and the solution of software defect and the corresponding solution of the software defect are generated for developer
Scheme generation module；
For the software defect and the corresponding solution of the software defect to be marked by mass classification
Mark module；
For the software defect to be divided into different type according to the label and obtains the defect classification mould of defect kind
Block；
The semantic concept module includes:
For generating the tag tree module of a tag tree added with semantic conceptual model by cosine similarity algorithm；
Wherein, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and phase
Close the relationship between semantic concept；
For receiving the user query module of user query request；
Inquiry request for being inputted by user recommends the label to user according to the related semantic concept
Label recommendations module；
The user feedback module includes:
Outcome evaluation, scoring and the evaluation module for proposing comment are carried out for user；
For managing the feedback information management module of field feedback；
For storing the feedback information storage module of field feedback；
For recording user's information logging modle of user's information of user.
Further, the system also includes:
It include client's input module of label and end face Information Problems for client's input；
For showing the display module of software deficiency report；
Existing data carry out matched data in keyword and end face information and date library for that will be used to input
With module；Wherein, end face information includes type, language and defect description；
Module is established for the label and the software defect and solution to be established associated association.
Further, the computation model of the cosine similarity algorithm are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect
bkIn weight, wkiValue be defined as TF-IDF, and have
It respectively indicates and is expressed as label ti,tjVector.N represents the sum of software defect, and n represents label tiAt least
There is the quantity of primary software defect.
A kind of software defect retrieval and analysis method based on semantic concept, which comprises
Classifying step for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept step of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback step of user's information；
The classifying step includes:
Software defect and the solution of software defect and the corresponding solution of the software defect are generated for developer
Scheme generates step；
For the software defect and the corresponding solution of the software defect to be marked by mass classification
Markers step；
For the software defect to be divided into different type according to the label and obtains the defect classification step of defect kind
Suddenly；
The semantic concept step includes:
For generating the tag tree step of a tag tree added with semantic conceptual model by cosine similarity algorithm；
Wherein, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and phase
Close the relationship between semantic concept；
For receiving the user query step of user query request；
Inquiry request for being inputted by user recommends the label to user according to the related semantic concept
Label recommendations step；
The user feedback step includes:
Outcome evaluation, scoring and the appraisal procedure for proposing comment are carried out for user；
For managing the feedback information management process of field feedback；
For storing the feedback information storing step of field feedback；
For recording user's information recording step of user's information of user.
Further, the method also includes:
For client input include label and end face information the problem of client's input step；
For showing the display step of software deficiency report；
Existing data carry out matched data in keyword and end face information and date library for that will be used to input
With step；Wherein, end face information includes type, language and defect description；
For the label and the software defect and solution to be established associated association establishment step.
Further, the computation model of the cosine similarity algorithm are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect
bkIn weight, wkiValue be defined as TF-IDF, and have
It respectively indicates and is expressed as label ti,tjVector.N represents the sum of software defect, and n represents label tiAt least
There is the quantity of primary software defect.
The invention has the advantages that:
A kind of software defect retrieval and analysis system and method based on semantic concept proposed by the present invention, the system and
Method can be supported with the semantic conceptual model of keyword search video with establishing and use to software retrieval and be with user feedback
The method of semantic concept based on the complementary sorting technique on basis.Developer and user pass through input keyword and progress one
A little association operations for being referred to as end face, so that it may search suitable software defect.The system and method use semantic general
Model is read to improve the accuracy of retrieval.In addition, the needs of in order to meet developer and user, this system allows them
Submit feedback information.This technology combines keyword search, end face search and public search carry out software defect retrieval and
Analysis.Meanwhile mass classification allows user that file is marked according to file concrete meaning on the net.Even if masses' classification
Method has the advantages that various, its main disadvantage still lacks semantic information.Therefore, for avoid mass classification it is this lack
Point improves the accuracy of semantic analysis using a kind of semantic conceptual model in the present invention, it improves retrieval and point
The efficiency of analysis allows developer and user thus to find relevant software defect and suitable solution.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of system of the present invention；
Fig. 2 is software defect search interface；
Fig. 3 is label recommendations interface；
Fig. 4 is the results list of software defect；
Fig. 5 is defect report display interface；
Fig. 6 is the example schematic that system of the present invention labels；
Fig. 7 is the architectural schematic of semantic conceptual model；
Fig. 8 is the method for the invention flow chart.
Specific embodiment
The present invention will be further described combined with specific embodiments below, but the present invention should not be limited by the examples.
Embodiment 1:
A kind of software defect retrieval and analysis system based on semantic concept, as shown in Figure 1, the system comprises:
Categorization module for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept module of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback module of user's information；
The categorization module includes:
Software defect and the solution of software defect and the corresponding solution of the software defect are generated for developer
Scheme generation module；
For the software defect and the corresponding solution of the software defect to be marked by mass classification
Mark module；
For the software defect to be divided into different type according to the label and obtains the defect classification mould of defect kind
Block；
The semantic concept module includes:
For generating the tag tree module of a tag tree added with semantic conceptual model by cosine similarity algorithm；
Wherein, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and phase
Close the relationship between semantic concept；
For receiving the user query module of user query request；
Inquiry request for being inputted by user recommends the label to user according to the related semantic concept
Label recommendations module；
The user feedback module includes:
Outcome evaluation, scoring and the evaluation module for proposing comment are carried out for user；
For managing the feedback information management module of field feedback；
For storing the feedback information storage module of field feedback；
For recording user's information logging modle of user's information of user.
Wherein, the system also includes:
It include client's input module of label and end face Information Problems for client's input；
For showing the display module of software deficiency report；
Existing data carry out matched data in keyword and end face information and date library for that will be used to input
With module；Wherein, end face information includes type, language and defect description；
Module is established for the label and the software defect and solution to be established associated association.
The software defect based on semantic concept that the present embodiment proposes is retrieved and analysis system mainly includes three modules:
Categorization module: this module is for classifying label.Developer create one it is new comprising defect
After the software deficiency report of description and associated solutions, it is marked it with label, according to class algorithm, this
Label will be arranged in a defect classification.
Semantic concept module: this module is the core of system.In this module, we have used semantic concept mould
Type.Semantic conceptual model is a hierarchy system, this structure forms (label and relevant semantic concept) by conceptional tree.It is semantic
The label of conceptual model description and the relationship of related semantic concept help user and developer to retrieve relevant software with this
Defect description and solution information.If user select a suitable semantic concept, our system will provide one it is soft
The results list of part defect.
User feedback module: user can weigh relevant solution after the software defect and associated solutions that system provides
Certainly scheme and comment is provided, this module management and the feedback information for storing user, while the information of user is also had recorded,
So as to improve system performance.
In the beginning of retrieving, developer and user can produce the solution of software defect and they, and
It is marked by adding label.Similar label will be put together by semantic analysis.If user has input comprising closing
The problem of keyword and end face information, our system will recommend respective labels.Developer and user can weigh these labels
To improve the quality of label recommendations.Finally, developer can be found by proposing recommended label software defect and
Corresponding solution.
A kind of software defect retrieval and analysis system based on semantic concept that the present embodiment proposes, the system comprises four
The analysis of the retrieval of a aspect: keyword and facet retrieval, public analytic approach, the feedback of semantic conceptual model and user.System
Retrieval analysis overall process are as follows: firstly, for inputting keyword " e-mail management " and the information of related fields,
Show that developer inputs the information of keyword " e-mail management " and related fields in Fig. 2.It is looked into when developer clicks
Button is ask come when searching for relevant defect, system will recommend label appropriate and relevant semantic concept.Fig. 3 shows recommendation
Label and relevant semantic concept.Developer can choose suitable label and concept.In this example, developer has selected label
" mail management " and related semantic concept " error in data ", for describing the software defect type of " mail management ".Fig. 4 is shown
Developer select mark of correlation and when semantic concept software defect the results list, 5 defect reports according to user scoring
It is sorted.Each report includes the information in relation to software deficiency report, such as program name, type, language.Developer
Arbitrary defect report be can choose to check the details of defect report.In Fig. 5, software deficiency report be described in detail to
Developer.This part of report shows the details of software " Yahoo Test ".Wherein, " user's scoring " represents the flat of user feedback
Respectively, " solution " represents the adjustment method of this defect.In software deficiency report, developer can be provided from 1 to 5
Feedback score and related commentary, with improve software bug retrieval and analysis quality.
The retrieval and analysis of each aspect are specific as follows:
It is retrieved for keyword and facet:
If developer merely enters suitable keyword and face information, relevant software deficiency report will be shown.If
Existing data are identical in the information and date library of developer's input, and system of the present invention can directly display relevant software and lack
Fall into report.Table 1 is a keyword input by user and face information.This request is described with one section of XML.It is crucial in table 1
Character table shows the keyword of user's input.Facet information includes " type ", " language ", " defect description ".
Table 1
Table 2 describes the obtained feedback of inquiry inputted according to Fig. 3.This feedback includes " software name ", " generally
It is bright ", " source code line number ", " type ", " defect description ", " defect cause ", " solution ".The core of this part of feedback information
Dividing is " defect description ", " defect cause ", " solution ".
Table 2
For mass classification:
Keyword of user's input for marking of defects is referred to as " label ".Developer can be lacked software with some labels
Fall into the expression of symbolism.Defect is categorized into several clusters by label.Fig. 6 illustrates the example to label.In this example,
It come the frequency of marking of defects " slow " is highest (8 times) with " mail management ", with " mail management ", come marking of defects, " test is wrong
Frequency accidentally " is highest (6 times), and therefore " slow " is identical with the classification of " test errors " and has identical label " mail pipe
Reason ".Finally, each defect can be classified.
For semantic conceptual model:
Label ambiguity is eliminated using semantic conceptual model, reinforces the accuracy of label recommendations with this.In systems, concept
The concept of network be used for it is every semantic concept is commonly understood by, software defect is described with this.So in our system
In, semantic conceptual model is made of label cluster and relevant semantic concept.In form, we have defined below.
Define 1: define tag tree: (i) tag tree is a structure of semantic conceptual model.(ii) t1, t2..., tk are
The sequence of label, they are comprising semantic concept and on a path from root to leaf.(iii) labelled tree is by label cluster and phase
Close semantic concept composition.
Define 2: in order to realize label cluster, it is necessary for calculate to the similarity degree between label.The present embodiment is adopted
The similarity degree between label is calculated with cosine similarity algorithm.The computation model of the cosine similarity algorithm are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect
bkIn weight, wkiValue be defined as TF-IDF, and have
It respectively indicates and is expressed as label ti,tjVector.N represents the sum of software defect, and n represents label tiAt least
There is the quantity of primary software defect.
Fig. 7 is an example with tree construction descriptive semantics conceptual model, and label cluster and semantic concept are shown in figure.Than
Such as, label " dynamic simulation module " and " embedded SL ", are classified into " simulator "." DATE ERROR " and " sequence errors "
Represent the semantic concept of " dynamic simulation module ".In fact, these semantic concepts explain software " dynamic simulation module "
Different defects.
The similitude that label is calculated by cosine similarity algorithm, by labeling cluster and forms semantic conceptual model.
Table 3 illustrates the detailed algorithm of semantic understanding.If the similarity of label and initial node ti are more than in this algorithm
Threshold value, this label just will become a child node for this tree；Otherwise, label and tree interior joint divide one kind into.Then, if
There are child nodes, and semantic concept to be just added to child node；Otherwise it is added to father node.Finally, gradually establishing semantic concept
Model.
Table 3
For the feedback of user:
As described in previous section, feedback module is for allowing user to carry out assessment appropriate to software error and providing comment.
The interest of feedback reflection user.It facilitates system and improves retrieval quality.The feedback information of user includes user to software defect
Scoring, the average score of software defect and the comment of user.
Embodiment 2
A kind of software defect retrieval and analysis method based on semantic concept, as shown in Figure 8, which comprises
Classifying step for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept step of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback step of user's information；
The classifying step includes:
Software defect and the solution of software defect and the corresponding solution of the software defect are generated for developer
Scheme generates step；
For the software defect and the corresponding solution of the software defect to be marked by mass classification
Markers step；
For the software defect to be divided into different type according to the label and obtains the defect classification step of defect kind
Suddenly；
The semantic concept step includes:
For generating the tag tree step of a tag tree added with semantic conceptual model by cosine similarity algorithm；
Wherein, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and phase
Close the relationship between semantic concept；
For receiving the user query step of user query request；
Inquiry request for being inputted by user recommends the label to user according to the related semantic concept
Label recommendations step；
The user feedback step includes:
Outcome evaluation, scoring and the appraisal procedure for proposing comment are carried out for user；
For managing the feedback information management process of field feedback；
For storing the feedback information storing step of field feedback；
For recording user's information recording step of user's information of user.
Wherein, the method also includes:
For client input include label and end face information the problem of client's input step；
For showing the display step of software deficiency report；
Existing data carry out matched data in keyword and end face information and date library for that will be used to input
With step；Wherein, end face information includes type, language and defect description；
For the label and the software defect and solution to be established associated association establishment step.
The computation model of the cosine similarity algorithm are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect
bkIn weight, wkiValue be defined as TF-IDF, and have
It respectively indicates and is expressed as label ti,tjVector.N represents the sum of software defect, and n represents label tiAt least
There is the quantity of primary software defect.
In the beginning of retrieving, developer creates software deficiency report and solution, split using mass classification
The defect report that originator is created is marked, and defect is divided into different classes (class 1-classN) according to label and obtains software
Then defect kind generates the tag tree for being added to semantic conceptual model using cosine similarity algorithm, inputs in user
After inquiry request, which will recommend relevant label to user according to semantic concept, and user checks the mark recommended
It scores after label current inquiry.Then our system is fed back.
Although the present invention has been disclosed in the preferred embodiment as above, it is not intended to limit the invention, any to be familiar with this
The people of technology can do various changes and modification, therefore protection of the invention without departing from the spirit and scope of the present invention
Range should subject to the definition of the claims.
Claims (6)
1. a kind of software defect retrieval and analysis system based on semantic concept, which is characterized in that the system comprises:
Categorization module for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept module of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback module of user's information；
The categorization module includes:
The software defect and solution of software defect and the corresponding solution of the software defect are generated for developer
Generation module；
Mark for the software defect and the corresponding solution of the software defect to be marked by mass classification
Remember module；
For the software defect to be divided into different type according to the label and obtains the defect categorization module of defect kind；
The semantic concept module includes:
For generating the tag tree module of a tag tree added with semantic conceptual model by cosine similarity algorithm；Its
In, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and correlation
Relationship between semantic concept；
For receiving the user query module of user query request；
Inquiry request for being inputted by user recommends the label of the label according to the related semantic concept to user
Recommending module；
The user feedback module includes:
Outcome evaluation, scoring and the evaluation module for proposing comment are carried out for user；
For managing the feedback information management module of field feedback；
For storing the feedback information storage module of field feedback；
For recording user's information logging modle of user's information of user.
2. software defect retrieval and analysis system according to claim 1, which is characterized in that the system also includes:
It include client's input module of label and end face Information Problems for client's input；
For showing the display module of software deficiency report；
Existing data carry out matched Data Matching mould in keyword and end face information and date library for that will be used to input
Block；Wherein, end face information includes type, language and defect description；
Module is established for the label and the software defect and solution to be established associated association.
3. software defect retrieval and analysis system according to claim 1, which is characterized in that the cosine similarity algorithm
Computation model are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect bkIn
Weight, wkiValue be defined as TF-IDF, and have
It is expressed as label ti,tjVector；N represents the sum of software defect, and n represents label tiAt least occur once
The quantity of software defect.
4. a kind of software defect retrieval and analysis method based on semantic concept, which is characterized in that the described method includes:
Classifying step for label to be classified；Wherein, the label is the keyword for marker software defect；
It is used to help the semantic concept step of user and developer's Checking label and solution information；
For managing and storing field feedback, while recording the user feedback step of user's information；
The classifying step includes:
The software defect and solution of software defect and the corresponding solution of the software defect are generated for developer
Generate step；
Mark for the software defect and the corresponding solution of the software defect to be marked by mass classification
Remember step；
For the software defect to be divided into different type according to the label and obtains the defect classifying step of defect kind；
The semantic concept step includes:
For generating the tag tree step of a tag tree added with semantic conceptual model by cosine similarity algorithm；Its
In, the semantic conceptual model includes label and related semantic concept, and the semantic conceptual model is for describing label and correlation
Relationship between semantic concept；
For receiving the user query step of user query request；
Inquiry request for being inputted by user recommends the label of the label according to the related semantic concept to user
Recommendation step；
The user feedback step includes:
Outcome evaluation, scoring and the appraisal procedure for proposing comment are carried out for user；
For managing the feedback information management process of field feedback；
For storing the feedback information storing step of field feedback；
For recording user's information recording step of user's information of user.
5. software defect retrieval and analysis method according to claim 4, which is characterized in that the method also includes:
For client input include label and end face information the problem of client's input step；
For showing the display step of software deficiency report；
Existing data carry out matched Data Matching step in keyword and end face information and date library for that will be used to input
Suddenly；Wherein, end face information includes type, language and defect description；
For the label and the software defect and solution to be established associated association establishment step.
6. software defect retrieval and analysis method according to claim 4, which is characterized in that the cosine similarity algorithm
Computation model are as follows:
Wherein, sim (ti,tj) similar value between label, label ti,tjIt is represented as vector；wkiIt is label tiIn defect bkIn
Weight, wkiValue be defined as TF-IDF, and have
It respectively indicates and is expressed as label ti,tjVector；N represents the sum of software defect, and n represents label tiAt least occur one
The quantity of secondary software defect.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113886467A (en) * | 2021-10-25 | 2022-01-04 | 上海航天计算机技术研究所 | Software defect library maintenance method based on keyword extraction |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101814098A (en) * | 2010-05-11 | 2010-08-25 | 天津大学 | Method for obtaining software security defects based on vertical search and semantic annotation |
| CN104951458A (en) * | 2014-03-26 | 2015-09-30 | 华为技术有限公司 | Method and equipment for helping processing based on semantic recognition |
| CN106649557A (en) * | 2016-11-09 | 2017-05-10 | 北京大学（天津滨海）新代信息技术研究院 | Semantic association mining method for defect report and mail list |
| CN107608732A (en) * | 2017-09-13 | 2018-01-19 | 扬州大学 | A kind of bug search localization methods based on bug knowledge mappings |
| CN106844194B (en) * | 2016-12-21 | 2018-06-15 | 北京航空航天大学 | A kind of construction method of multi-level software fault diagnosis expert system |
-
2018
- 2018-10-31 CN CN201811285850.5A patent/CN109299381B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101814098A (en) * | 2010-05-11 | 2010-08-25 | 天津大学 | Method for obtaining software security defects based on vertical search and semantic annotation |
| CN104951458A (en) * | 2014-03-26 | 2015-09-30 | 华为技术有限公司 | Method and equipment for helping processing based on semantic recognition |
| CN106649557A (en) * | 2016-11-09 | 2017-05-10 | 北京大学（天津滨海）新代信息技术研究院 | Semantic association mining method for defect report and mail list |
| CN106844194B (en) * | 2016-12-21 | 2018-06-15 | 北京航空航天大学 | A kind of construction method of multi-level software fault diagnosis expert system |
| CN107608732A (en) * | 2017-09-13 | 2018-01-19 | 扬州大学 | A kind of bug search localization methods based on bug knowledge mappings |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113886467A (en) * | 2021-10-25 | 2022-01-04 | 上海航天计算机技术研究所 | Software defect library maintenance method based on keyword extraction |
| CN113886467B (en) * | 2021-10-25 | 2024-05-14 | 上海航天计算机技术研究所 | Software defect library maintenance method based on keyword extraction |
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