CN110990291A - Regression test case priority ranking method based on node importance evaluation - Google Patents

Abstract

The invention relates to the technical field of regression testing of object-oriented software systems, and provides a regression testing case priority ranking method based on node importance evaluation. Firstly, converting an object-oriented software system into a class-level directed complex network based on information flow; then, node C is calculated_iAnd node C_iThe threat, vulnerability and potential safety hazard between the neighboring nodes, and based on the probability risk assessment method, the node C is calculated_iAnd node C_jThe weight value between; and finally, calculating the PageRank value of each node based on a PageRank algorithm, and sequencing the priority of the test cases corresponding to the nodes according to the PageRank values of the nodes, wherein the higher the PageRank value is, the higher the priority of the test cases corresponding to the nodes is. The invention can reduce the computational complexity of the regression test case priority sequence, compress the computational cost and improve the computational efficiency.

Description

Regression test case priority ranking method based on node importance evaluation

Technical Field

The invention relates to the technical field of regression testing of object-oriented software systems, in particular to a regression testing case priority ranking method based on node importance evaluation.

Background

Currently, computer systems and mobile communications are continuously developed, and software functions provided by the computer systems and the mobile communications are rich and diverse and become indispensable in daily life of people gradually. The whole software development process comprises the stages of requirement analysis, software design, software testing, software operation and maintenance and the like. The software testing stage is an auditing or comparing process between actual output and expected output, the process is not only complicated, but also the cost is expensive, and particularly in the large-scale software development process, an effective software testing method is found, which has the important effects of improving the software quality and reducing the software cost.

Regression testing refers to re-testing after old code has been modified to confirm that the modification did not introduce new errors or cause other code errors. Due to factors such as time and cost, the center of gravity of regression testing is centered on a critical module, and testers hope to sort test cases in a certain way so that the test cases with higher priorities can run earlier.

Most of the existing regression test case priority ranking methods perform priority ranking on test cases through static call graphs of tested programs or through methods of clustering the test cases and the like, and the methods only depend on the complexity of codes and ignore risk factors existing in the running process of software.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a regression test case priority ranking method based on node importance evaluation, which can reduce the calculation complexity and the compression calculation cost of the regression test case priority ranking and improve the calculation efficiency.

The technical scheme of the invention is as follows:

a regression test case priority ranking method based on node importance evaluation is characterized by comprising the following steps:

step 1: converting an object-oriented software system into a class-level directed complex network based on information flow;

step 1.1: representing the object-oriented software system ω as ω ═ C₁,C₂,...,C_i,...,C_I}; wherein, C_iThe method comprises the steps that the method is an ith class in an object-oriented software system omega, I belongs to {1, 2.,. I }, and I is the total number of classes in the object-oriented software system omega; c_i＝{m_i1,m_i2,...,m_ip,...,m_i,Pi,a_i1,a_i2,...,a_iq,...,a_i,Qi}，m_ipIs of class C_iThe p-th method of (1), a_iqIs of class C_iIs the qth attribute in (1), P ∈ {1,2_i}，q∈{1,2,...,Q_i}，P_iIs of class C_iTotal number of methods in (1), Q_iIs of class C_iTotal number of attributes in (1);

step 1.2: each class C_iSet as a node, if node C_hTo node C_kPassing information, node C is connected_hAnd node C_kFormed with directed edges<C_h,C_k>Converting an object-oriented software system omega into a class-level directed complex network based on information flow; wherein, C_h∈ω，C_kE is omega, and h is not equal to k;

step 2: calculating weights among nodes based on a probability risk evaluation method;

step 2.1: note node C_iThe neighbor node of (C)_j，C_j∈W_i，W_iIs node C_iThe neighbor node set of (2); computing node C_iAnd node C_jT between_ij＝ID_ij+OD_ij(ii) a Wherein, ID_ijIs node C_jTo node C_iTotal number of information streams transferred, OD_ijIs node C_iTo node C_jTotal number of information streams passed;

step 2.2: computing node C_iAnd node C_jHas a vulnerability of

Wherein, length_ipIs a method m_ipTotal number of code lines, PI_ipjIs a method m_ipSlave node C_jTotal number of received parameters, PO_ipjIs a method m_ipSlave node C_iIs transmitted to the node C_jThe total number of parameters of (a);

step 2.3: computing node C_iAnd node C_jThe potential safety hazard between is

Wherein x is_ipjIs node C_jMiddle exposure method m_ipTotal number of methods affected by failure, x_jpiIs node C_iMiddle exposure method m_jpTotal number of methods affected by failure;

step 2.4: calculating node C based on probability risk assessment method_iAnd node C_jThe weight value between is

And step 3: carrying out priority sequencing on the test cases based on a PageRank algorithm;

step 3.1: computing node C_iHas a PageRank value of

Wherein σ is a damping coefficient, I_iIs node C_iSet of neighbor nodes W_iTotal number of nodes in;

step 3.2: and sequencing the priority of the test cases corresponding to the nodes according to the PageRank values of the nodes, wherein the higher the PageRank value is, the higher the priority of the test cases corresponding to the nodes is.

The invention has the beneficial effects that:

1. the invention converts an object-oriented software system into a class-level directed complex network based on information flow, converts the problem of case priority ordering in the regression test into the problem of node importance calculation in the weighted complex network, reduces the calculation complexity of the problem and improves the efficiency of case priority ordering in the regression test.

2. The method calculates the threat, the vulnerability and the potential safety hazard between the nodes based on the probability risk assessment method, thereby obtaining the weight between the nodes, fully considering the risk factors existing in the software in the operation process, simply and accurately determining the specific value of each parameter, compressing the calculation cost and saving the calculation time.

3. Based on the PageRank algorithm, the method converts the problem of measuring the importance degree of a specific webpage relative to other webpages in the search engine index into the problem of determining the importance of different nodes in a complex network, and can quickly sequence the priority of test cases according to the PageRank values of the nodes.

Drawings

FIG. 1 is a flowchart of a regression test case prioritization method based on node importance assessment in accordance with the present invention;

fig. 2 is a schematic diagram of converting an object-oriented software system into a class-level directed complex network based on information flow in a specific embodiment.

Detailed Description

The invention will be further described with reference to the accompanying drawings and specific embodiments.

As shown in fig. 1, the regression test case priority ranking method based on node importance evaluation of the present invention includes the following steps:

step 1: converting an object-oriented software system into a class-level directed complex network based on information flow;

step 1.1: representing the object-oriented software system ω as ω ═ C₁,C₂,...,C_i,...,C_I}; wherein, C_iThe method comprises the steps that the method is an ith class in an object-oriented software system omega, I belongs to {1, 2.,. I }, and I is the total number of classes in the object-oriented software system omega; c_i＝{mi1,mi2,...,mip,...,m_i,Pi,ai1,ai2,...,aiq,...,a_i,QiMip is class C_iThe p-th method in (1), aiq, is class C_iIs the qth attribute in (1), P ∈ {1,2_i}，q∈{1,2,...,Q_i}，P_iIs of class C_iTotal number of methods in (1), Q_iIs of class C_iTotal number of attributes in (1);

step 1.2: as shown in FIG. 2, each class C is divided into_iSet as a node, if node C_hTo node C_kPassing information, node C is connected_hAnd node C_kFormed with directed edges<C_h,C_k>Converting an object-oriented software system omega into a class-level directed complex network based on information flow; wherein, C_h∈ω，C_kE ω, and h ≠ k.

Step 2: calculating weights among nodes based on a probability risk assessment method (PRA);

step 2.1: note node C_iThe neighbor node of (C)_j，C_j∈W_i，W_iIs node C_iThe neighbor node set of (2); computing node C_iAnd node C_jT between_ij＝ID_ij+OD_ij(ii) a Wherein, ID_ijIs node C_jTo node C_iTotal number of information streams transferred, OD_ijIs node C_iTo node C_jTotal number of information streams passed;

step 2.2: computing node C_iAnd node C_jHas a vulnerability of

Wherein, length_ipIs a method m_ipTotal number of code lines, PI_ipjIs a method m_ipSlave node C_jTotal number of received parameters, PO_ipjIs a method m_ipSlave node C_iIs transmitted to the node C_jThe total number of parameters of (a);

step 2.3: computing node C_iAnd node C_jThe potential safety hazard between is

Wherein x is_ipjIs node C_jMiddle exposure method m_ipTotal number of methods affected by failure, x_jpiIs node C_iMiddle exposure method m_jpTotal number of methods affected by failure;

step 2.4: calculating node C based on probability risk assessment method_iAnd node C_jThe weight value between is

And step 3: carrying out priority sequencing on the test cases based on a PageRank algorithm;

and 3. step 3.1: computing node C_iHas a PageRank value of

Wherein σ is a damping coefficient, I_iIs node C_iSet of neighbor nodes W_iTotal number of nodes in;

step 3.2: and sequencing the priority of the test cases corresponding to the nodes according to the PageRank values of the nodes, wherein the higher the PageRank value is, the higher the priority of the test cases corresponding to the nodes is. Wherein, the higher the PageRank value is, the higher the importance of the representative node is, and the node needs to be tested preferentially.

It is to be understood that the above-described embodiments are only a few embodiments of the present invention, and not all embodiments. The above examples are only for explaining the present invention and do not constitute a limitation to the scope of protection of the present invention. All other embodiments, which can be derived by those skilled in the art from the above-described embodiments without any creative effort, namely all modifications, equivalents, improvements and the like made within the spirit and principle of the present application, fall within the protection scope of the present invention claimed.

Claims (1)

1. A regression test case priority ranking method based on node importance evaluation is characterized by comprising the following steps:

step 1: converting an object-oriented software system into a class-level directed complex network based on information flow;

step 1.1: representing the object-oriented software system ω as ω ═ C₁,C₂,...,C_i,...,C_I}; wherein, C_iThe method comprises the steps that the method is an ith class in an object-oriented software system omega, I belongs to {1, 2.,. I }, and I is the total number of classes in the object-oriented software system omega;

m_ipis of class C_iThe p-th method of (1), a_iqIs of class C_iIs the qth attribute in (1), P ∈ {1,2_i}，q∈{1,2,...,Q_i}，P_iIs of class C_iTotal number of methods in (1), Q_iIs of class C_iTotal number of attributes in (1);

step 1.2: each class C_iSet as a node, if node C_hTo node C_kPassing information, node C is connected_hAnd node C_kFormed with directed edges<C_h,C_k>Converting an object-oriented software system omega into a class-level directed complex network based on information flow; wherein, C_h∈ω，C_kE is omega, and h is not equal to k;

step 2: calculating weights among nodes based on a probability risk evaluation method;

step 2.1: note node C_iThe neighbor node of (C)_j，C_j∈W_i，W_iIs node C_iThe neighbor node set of (2); computing node C_iAnd node C_jT between_ij＝ID_ij+OD_ij(ii) a Wherein, ID_ijIs node C_jTo node C_iTotal number of information streams transferred, OD_ijIs node C_iTo node C_jTotal number of information streams passed;

step 2.2: computing node C_iAnd node C_jHas a vulnerability of

Wherein, length_ipIs a method m_ipTotal number of code lines, PI_ipjIs a method m_ipSlave node C_jTotal number of received parameters, PO_ipjIs a method m_ipSlave node C_iIs transmitted to the node C_jThe total number of parameters of (a);

step 2.3: computing node C_iAnd node C_jThe potential safety hazard between is

Wherein x is_ipjIs node C_jMiddle exposure method m_ipTotal number of methods affected by failure, x_jpiIs node C_iMiddle exposure method m_jpLoss ofTotal number of methods affected by effect;

step 2.4: calculating node C based on probability risk assessment method_iAnd node C_jThe weight value between is

And step 3: carrying out priority sequencing on the test cases based on a PageRank algorithm;

step 3.1: computing node C_iHas a PageRank value of

Wherein σ is a damping coefficient, I_iIs node C_iSet of neighbor nodes W_iTotal number of nodes in;

step 3.2: and sequencing the priority of the test cases corresponding to the nodes according to the PageRank values of the nodes, wherein the higher the PageRank value is, the higher the priority of the test cases corresponding to the nodes is.

Images