How Is Source Code Analysis Performed? Methodology, Tools and Reporting

From scoping to retest: the step-by-step methodology of professional source code analysis, tool selection and reporting standards.

Source code analysis can often look like a simple operation from the outside.

The code is obtained.

A SAST tool is run.

A list of findings is produced.

A report is prepared.

But a professional source code security analysis is far more comprehensive than that.

Because the real aim is not merely to produce tool output.

The real aim is:

to surface the genuine, exploitable and business-relevant security risks inside the application's source code.

To do that, the analysis process must be planned correctly.

Which repositories will be examined,

which branch will be used,

which technologies are present,

how the application's architecture works,

which modules are critical,

which user roles exist,

which external services it integrates with

all directly affect the result of the analysis.

In the same way, using only an automated tool may not be sufficient.

An automated SAST analysis can scan a broad code base quickly.

But manual source code analysis may be needed in areas such as authentication, authorisation, business logic, cryptography and critical transaction flows.

Professional source code analysis therefore usually consists of a combination of several different security layers:

Scoping → Architecture Analysis → Automated SAST → Manual Code Review → Finding Validation → Risk Rating → Reporting → Remediation → Retest

In this chapter we will look step by step at how source code analysis should be conducted from beginning to end.

1. Where Does a Source Code Analysis Project Begin?

The first stage is not scanning the code but understanding the scope.

Before a security analysis begins, these questions must be answered:

Which application will be analysed?
Which repositories are in scope?
Which branch or tag will be examined?
Is the code analysed the same as production?
Which programming languages are used?
Which frameworks are in use?
Which are the application's critical modules?
Which user roles exist?
What types of data does the application process?
Which external systems does it integrate with?
Is the application internet-facing?

This information is not only for project management.

It also determines the depth and priorities of the security test.

A payment system and a simple corporate portal do not have the same security profile, for example.

The same methodology should therefore not be applied identically.

2. What Is Scope?

Scope defines which systems and components the security analysis will cover.

From a source code analysis perspective, scope can include these components:

Backend repository
Frontend repository
Mobile application
API services
Microservice repositories
Shared libraries
Authentication service
Infrastructure as Code
CI/CD configuration
Config files

The more clearly scope is defined, the more soundly the analysis is conducted.

An unclear scope can create wrong expectations for both the security team and the development team.

3. Which Branch Should Be Analysed?

This is an important but often overlooked matter.

A repository can contain different branches.

For example:

main
develop
release
feature
hotfix

It matters that the code analysed matches the code running in the production environment.

Otherwise the vulnerabilities found or not found may not reflect the real production situation.

Where possible, therefore, a:

Release Tag

or the commit hash matching production should be used.

This information should also be stated clearly in the report.

4. Why Should the Commit Hash Be Recorded?

Source code changes constantly.

The code analysed today may be different tomorrow.

Which code version was examined during the analysis must therefore be recorded.

For example, information can be held as:

Repository: Payment API

Branch: release/3.2

Commit: a13f72c...

Which changes were made can then be tracked more accurately during the retest.

5. How Should Source Code Be Shared Securely?

Source code is one of an organisation's most valuable intellectual property assets.

Code sharing must therefore be handled carefully from a security perspective.

Where possible:

controlled Git access,
VPN,
an internal repository,
secure temporary access

should be used.

Sharing source code as an open email attachment is not ideal.

How the code will be stored after the analysis and when it will be deleted should also be determined in advance.

6. Why Does Source Code Confidentiality Matter?

Source code does not only show how the application works.

It can also reveal:

the business logic,
infrastructure names,
API endpoints,
security controls,
integration structures.

A confidentiality agreement and access control are therefore important in a source code analysis engagement.

In defence, finance, public sector and critical infrastructure projects in particular, whether the source code is uploaded to an external system should also be questioned.

7. Cloud SAST or On-Premise SAST?

When choosing a SAST solution, the deployment model matters.

In a cloud SAST solution, source code or data derived from it can be sent to the service provider's infrastructure.

In an on-premise solution, the analysis can be carried out in the organisation's own environment.

In critical projects these questions should be asked:

Does the code leave the organisation?
Where is the analysis data held?
Does the provider store the code?
What is the retention period?
Which country or region is used?
Is there a data residency requirement?

The data security model should be assessed as much as the technical features.

8. Producing a Technology Inventory

One of the first technical stages of the analysis is determining the technology stack.

For example:

Backend: Java / Spring Boot

Frontend: React / TypeScript

Database: PostgreSQL

Authentication: OAuth 2.0 / JWT

Build: Maven

CI/CD: GitLab CI

a technology map like this can be created.

This information determines which SAST rules will be used and which security risks will be prioritised in the manual analysis.

9. Why Does Framework Knowledge Matter?

The same programming language can have different security behaviours in different frameworks.

A Java application may be using Spring Security, for example.

Authorisation can be applied with annotations.

Another Java application may have a bespoke authentication framework.

The security code review method for these two applications is not the same.

The SAST tool understanding the framework correctly also seriously affects the false positive rate.

10. Architecture Analysis

Before source code analysis begins, the application's architecture must be understood.

Is it a monolith?

Is it microservices?

Is there an API gateway?

Is authentication centralised?

How do the services communicate with one another?

From which layer is database access made?

This information helps in understanding the security-critical data flows.

11. Can a Data Flow Diagram Be Used?

Yes.

A data flow diagram is quite useful particularly in complex systems.

For example, the data flow can be drawn as:

User

↓

Web Application

↓

API Gateway

↓

Payment Service

↓

Database

Trust boundary points can then be identified.

This approach lets manual code analysis be performed on a more risk-based footing.

12. Does Threat Modeling Strengthen Source Code Analysis?

Yes.

Doing basic threat modeling before source code analysis begins can make the critical areas visible.

In the system:

payments,
password reset,
file upload,
admin operations

may be high-risk, for example.

These functions can be prioritised during manual code review.

More effective security analysis is thus performed among millions of lines of code.

13. Asset Identification

The critical assets that must be protected in the application should be identified.

For example:

customer data,
credit card information,
authentication tokens,
financial transaction records,
personal data,
encryption keys.

How these assets are handled inside the code is analysed.

The basic question should be:

Which data will the attacker try to reach?

14. Entry Point Analysis

The points at which an attacker can send data to the application are identified.

Entry points such as:

HTTP GET
HTTP POST
API body
Header
Cookie
File Upload
Message Queue
WebSocket

can be found.

These points are strong starting areas for manual code review.

15. Source Analysis

From a SAST perspective, entry points are mostly assessed as sources.

User-controlled data is traced through the code.

The aim is to answer the question:

Which critical functions can this data reach?

This analysis is critically important in detecting injection and data-handling problems.

16. Sink Analysis

Sinks are the security-critical functions.

For example, functions such as:

SQL execute
OS command execution
file read/write
HTTP request
HTML output
XML parser

can be sinks.

In source code analysis, the relationship of these functions with user-controlled data is assessed.

17. Source-to-Sink Analysis

An example data flow might be:

HTTP Parameter

↓

Controller

↓

Service

↓

Repository

↓

SQL Query

The aim here is to understand whether user input is processed safely before it reaches the SQL query.

SAST tools can perform this analysis automatically.

The manual specialist verifies the result, particularly in complex or bespoke framework structures.

18. The First SAST Scan

Once the technology stack has been determined, the first scan can be carried out with a suitable SAST profile.

This first scan generally establishes a baseline.

Hundreds or thousands of findings can appear in the results.

But not all of that output should be accepted as a genuine vulnerability.

The first stage is raw security data.

19. What Is a Baseline Scan?

A baseline scan is the first comprehensive scan that sets out the application's current security position.

For example, findings of:

7 Critical
35 High
182 Medium
640 Low

can be seen.

This number should not be presented to the customer directly.

Triage is needed first.

Because a significant part of it can be false positives or duplicates.

20. Why Is Tool Output Not the Final Report?

Presenting the export file produced by a SAST tool to the customer as a report is not professional source code analysis.

Tool results can contain:

false positives,
duplicates,
not-reachable items,
test code,
dead code.

Every finding must therefore be assessed in context.

This stage is called security triage.

21. Security Triage

During triage the findings are examined.

For every finding these questions can be asked:

Is the data really user-controlled?
Does the risky code really run?
Is it used in production?
Is the security control present in another layer?
Does the framework make this operation safe automatically?
Can an attacker reach this point?

This process significantly lowers the false positive rate.

22. Duplicate Findings

The same security problem can be reported repeatedly across different data flows.

A single insecure helper function may be called by 20 different endpoints, for example.

SAST can report that as 20 separate findings.

But the root cause may be a single function.

A professional report should show that relationship.

23. Root Cause Analysis

One of the most valuable stages of source code analysis is root cause analysis.

If there are 15 SQL Injection findings, for example, they may all stem from the same unsafe database helper.

In that case the shared helper can be made secure instead of fixing 15 lines of code one by one.

This approach significantly reduces the cost of remediation.

24. When Does Manual Source Code Analysis Begin?

Manual analysis can be carried out after SAST triage or in parallel with it.

Manual analysis focuses particularly on high-risk areas.

For example:

Authentication
Authorization
Payment
Admin functions
Cryptography
File upload
Password reset
Token management
External integrations

can be priorities.

25. Authentication Code Analysis

When authentication code is examined, these areas can be assessed:

Login logic
Password hashing
Account lockout
MFA
Session creation
Token generation
Password reset
Logout
Token revocation

These areas are critical security functions.

A small error can escalate as far as account takeover.

26. Authorization Analysis

Authorization code review is one of the most important areas of manual analysis.

The question:

Can this user really access this resource?

should be assessed for every critical operation.

A login check alone is not sufficient.

Object-level and function-level authorisation must be examined together.

27. Using a Role Matrix

A role matrix can be prepared for complex applications.

For example:

Function	User	Manager	Admin
View own profile	Yes	Yes	Yes
View another user	No	Limited	Yes
Delete a user	No	No	Yes

During manual code review the actual code is compared against this business rule.

Broken Access Control problems are thus detected more easily.

28. Business Logic Analysis

Business logic is one of the areas automated tools struggle with most.

Business rules such as:

discount limits,
payment ordering,
transfer limits,
approval mechanisms,
reservation rules

can be examined inside the code.

Here the security specialist must understand how the application works.

29. Financial Logic Review

In applications performing financial transactions, areas such as:

amount manipulation,
currency conversion,
transaction replay,
approval bypass,
negative values,
rounding errors

can be assessed in particular.

These problems cannot always be found by classic SAST rules.

30. Input Validation Review

How user inputs are validated is examined.

Checks on:

format,
length,
range,
allowed characters,
business constraints

can be assessed.

But input validation is not the main solution to every security problem.

A parameterized query is still required for SQL Injection, for example.

31. Output Encoding Review

In web applications in particular, how user-controlled data is written to output is examined.

HTML,

JavaScript,

URL,

CSS

have different security contexts.

Context-aware encoding must be applied.

32. Database Security Review

At the database access layer these points can be examined:

prepared statements,
raw queries,
unsafe ORM functions,
dynamic queries,
excessive DB privileges,
credential management.

Whether database errors are reflected back to the user can also be checked.

33. File Handling Review

File operations are among the high-risk areas.

These functions can be examined:

upload,
download,
delete,
archive extraction,
filename generation,
path construction.

Problems such as path traversal and unsafe upload can emerge here.

34. SSRF Review

The functions where the server sends requests to external systems are examined.

Features such as:

URL preview,
webhooks,
image import,
PDF generation,
callbacks

can carry SSRF risk.

Allowlist and network restriction controls are assessed.

35. Cryptography Review

Cryptography analysis is not only checking which algorithm is used.

All of these areas matter:

algorithm,
key size,
key storage,
IV,
random generation,
password hashing,
signing.

The use of hard-coded keys in particular can be a critical risk.

36. Random Number Review

A cryptographically secure random number generator must be used for security-critical tokens.

For example:

password reset tokens,
session IDs,
verification codes,
API tokens

must not be produced with an ordinary pseudo-random function.

37. Logging Review

Logging can be both a security control and a source of data leakage.

These can be examined:

are passwords being logged?
are tokens being logged?
is PII being logged in the clear?
are failed logins being logged?
are admin actions being audited?

Good logging records the right events but does not expose secrets or sensitive data.

38. Error Handling Review

If a detailed stack trace is shown to the user in production, information disclosure can occur.

The exception handling structure is checked in the manual analysis.

Whether:

generic error responses,
secure internal logging,
exception sanitisation

are applied can be assessed.

39. Secret Review

Secret scanning can be performed on the source code and repository history.

Searches are made in particular for:

API keys
DB passwords
cloud credentials
private keys
tokens

Active secrets found can be treated as critical.

40. Dependency Review

Third-party components are analysed with SCA.

For example:

package,
version,
CVE,
dependency path,
fixed version

are determined.

SCA results must be triaged like SAST results.

41. Reachability Review

Whether the vulnerable function of the risky dependency is really used is assessed.

This is important particularly in large dependency lists for determining risk priority.

42. Configuration Review

Security configuration files can also be present in the source code repository.

For example:

CORS config
auth config
Spring Security
nginx template
application config

can be examined.

Incorrect configuration can create a security problem as critical as the code.

43. Infrastructure as Code Review

If in scope, Terraform, Kubernetes YAML and other IaC files can be analysed.

Problems such as:

public resources,
overly permissive IAM,
privileged containers,
disabled encryption

can be detected.

44. CI/CD Configuration Review

The source code may be secure while the pipeline is insecure.

In critical projects, therefore, matters such as:

pipeline credentials,
branch protection,
deployment permissions,
third-party actions,
artifact integrity

can be assessed.

45. Build Script Review

Build scripts matter from a supply chain risk perspective.

A script may be downloaded from the internet during the build without verification, for example.

Package integrity may not be checked.

A secret may be used inside a command line argument.

These can be taken into the analysis scope too.

46. How Are Manual Review and SAST Results Combined?

SAST and manual analysis can produce different findings.

SAST might find:

SQL Injection

Manual analysis might detect:

Authorization Bypass

The report needs to combine these two sources under a single risk model.

From the customer's perspective, the real risk matters more than which method found the finding.

47. Finding Validation

Every finding should be validated as far as possible.

An SQL Injection may have been detected as a potential issue, for example.

The source-to-sink flow is verified through code analysis.

Where possible, runtime verification can also be done safely in a test environment.

This approach raises the reliability of the finding.

48. Is a Proof of Concept Necessary?

It is not necessary to develop an exploit for every source code finding.

But for critical findings a controlled proof of concept can make the real risk easier to understand.

The impact can be shown particularly for findings such as:

RCE
Authentication Bypass
Authorization Bypass
SQL Injection

Of course the test environment and authorisation limits must be respected.

49. Using CVSS in Source Code Analysis

CVSS can be used to determine technical severity.

But the CVSS score alone does not fully show the real business risk.

A function with a high CVSS score may not be used in production, for example.

CVSS should therefore be supported with this information:

Reachability
Exposure
Data sensitivity
Business criticality
Exploitability

50. CWE Mapping

Mapping every security finding to a suitable CWE category as far as possible is useful.

It helps organisations measure long-term security trends.

An organisation might see, for example:

CWE-79 type problems fell by 40% in six months.

That shows the effect of the Secure Coding programme.

51. OWASP Mapping

In web and API applications, findings can also be mapped to OWASP risk categories.

This can make it easier for non-technical managers to understand the results in particular.

But the OWASP Top 10 should not be seen as a complete technical taxonomy covering every vulnerability.

It is stronger used together with CWE.

52. How Should Risk Severity Be Determined?

Example levels:

Critical

System takeover or very high business impact.

High

Serious data or privilege risk.

Medium

Meaningful security impact under certain conditions.

Low

Limited impact or a defence-in-depth issue.

Informational

May not be a vulnerability directly but is an improvement recommendation.

This classification can vary according to the organisation's risk model.

53. Distinguishing Technical Severity from Business Risk

This distinction should be considered in an ideal report.

A finding that is technically medium can be a high business risk if it is in a critical payment function.

A technically high finding can carry lower real risk if it is in an unused demo function.

An AppSec report should therefore not present only the scanner's severity.

54. Exploitability

How easily the finding can be exploited is assessed.

Questions such as:

is authentication required?
is a special condition required?
is it reachable from the internet?
is user interaction required?

are important.

55. Impact

What could happen if the attack succeeds?

Impacts such as:

data leakage,
privilege escalation,
manipulation of financial transactions,
account takeover,
remote code execution

are possible.

The report must explain the real consequence of the risk clearly.

56. What Is Remediation?

Remediation is the process of fixing the vulnerability.

A good source code analysis does not only show the problem.

It also explains how to fix it.

For example:

Problem: SQL built with string concatenation.

Remediation: Parameterized query.

But the solution must suit the technology stack.

57. Is Giving a Secure Code Example Useful?

Yes.

It is quite valuable particularly in developer-focused reports.

An unsafe versus secure example comparison can reduce remediation time.

But the example must suit the application's real framework and language.

Generic pseudocode is sometimes not enough.

58. How Detailed Should Remediation Guidance Be?

It can vary according to the finding's risk.

A short recommendation may be sufficient for a low-level missing security header, for example.

For a complex authorisation problem:

root cause,
affected flow,
secure architecture recommendation,
test case

should be given in more detail.

59. A Developer-Friendly Report

A good report must be usable by the developer.

Every finding should where possible contain:

Title
Severity
Affected File
Line Number
Description
Root Cause
Attack Scenario
CWE
OWASP
Remediation
Secure Code Example

This format turns a security report into action.

60. Should the Executive Summary Be Separate?

Yes.

A technical report can run to hundreds of pages.

Senior management is not expected to examine lines of code.

The executive summary can focus on:

total risk,
number of critical findings,
the most important risk areas,
recurring weaknesses,
recommended priorities.

The same report then serves both the technical and the management side.

61. Risk Distribution

The risk distribution can be shown in the report.

For example:

Critical: 2

High: 7

Medium: 18

Low: 31

But numbers alone are not sufficient.

The three most critical risks should also be explained.

62. CWE Distribution

Recurring weakness categories can be reported.

For example:

Authorization: 28%

Input Validation: 22%

Secret Management: 15%

These results provide valuable data for developer training.

63. Root Cause Summary

50 findings may stem from seven basic root causes, for example.

Showing that is more valuable than the individual finding count.

Because the organisation gets an answer to the question:

Why do we produce vulnerabilities?

This increases AppSec maturity.

64. Remediation Prioritisation

Not all findings can be fixed at once.

Prioritisation must therefore be done.

Example:

Priority 1

Internet-facing critical / high.

Priority 2

Authentication and authorisation.

Priority 3

Sensitive data and secret risks.

Priority 4

Medium security debt.

This approach directs team resources correctly.

65. Remediation Workshop

In complex projects, delivering the report alone may not be sufficient.

The security and development teams can hold a remediation workshop together.

In that meeting:

critical findings,
root causes,
safe implementation,
ownership

are assessed.

This is quite useful particularly in first source code analysis projects.

66. Assigning Finding Owners

Every finding must have an owner.

Otherwise findings can stay in the report.

Owners such as:

Authentication Team

Payment Team

Platform Team

can be assigned.

Integration with the ticketing system can be made.

67. Setting SLAs

A remediation SLA can be set according to risk level.

For example:

Critical: Urgent

High: Priority

Medium: Planned sprint

Low: Backlog

The organisation's own risk management approach and regulations must be taken into account.

68. What Is a Retest?

After developers fix the findings, the security team verifies the fix.

This stage is called a retest.

The aim is not merely to see whether the code has changed.

It is to check whether the vulnerability has genuinely been closed.

69. Can a New Vulnerability Arise During Retest?

Yes.

Sometimes remediation can create a new security problem.

While fixing the SQL Injection, the developer may have added a new authorisation check and broken another flow, for example.

Regression testing is therefore important particularly for critical changes.

70. Security Regression Testing

An automated test can be created so that a previously found vulnerability does not reappear.

For example:

A particular authorisation bypass was found in a pentest.

After it is fixed, a unit or integration security test can be added.

The same problem is then caught automatically in future.

This is quite a mature DevSecOps approach.

71. Can a Pentest Finding Be Turned into a SAST Rule?

In some cases yes.

Insecure use of a helper in the organisation's internal framework may have been discovered during a pentest, for example.

The AppSec team writes a custom SAST rule.

From then on, all repositories are scanned automatically for the same pattern.

This scales security knowledge.

72. How Often Should Source Code Analysis Be Done?

This depends on the application's development frequency.

In continuously developed projects, automated SAST can run on every pull request or build.

Manual source code analysis can be done after:

a major release,
an architectural change,
an authentication change,
a critical new feature.

73. When Should a Full Scan Be Run?

A full SAST scan can take time in large projects.

The model:

Pull Request → Incremental Scan

Nightly / Weekly → Full Scan

can therefore be used.

This provides a balance between speed and coverage.

74. Pre-Release Security Check

Before a critical release, the results of:

SAST
SCA
Secret Scanning
DAST
Pentest

can be assessed in a single risk view.

If there are vulnerabilities above a certain level, the release can be taken into a risk acceptance process.

75. The Relationship Between Source Code Analysis and the Secure SDLC

Source code analysis is only one stage of the Secure SDLC.

Before it:

threat modeling

and secure coding.

After it:

DAST,

pentesting,

monitoring.

A model integrated into the whole life cycle is therefore stronger than a one-off analysis.

76. Tools Used in Source Code Analysis

SAST products can differ.

Some tools work at enterprise scale.

Some can be open source.

Some can be stronger in particular programming languages.

In tool selection, criteria such as:

language support,
framework support,
data flow capability,
CI/CD integration,
false positives,
custom rules

matter.

The tool's name is not more important than the methodology.

77. Is a Single SAST Tool Enough?

Not always.

Different tools can be strong on different rules.

But using more than one tool can also increase finding noise.

More important than the number of tools is:

correct tuning + manual verification

78. What Is a Custom Rule?

A security rule specific to the organisation's own development framework can be written.

For example, use of the internal function named:

unsafeExecute()

can be prohibited.

SAST can create a finding automatically when it sees that function.

This turns the organisation's security knowledge into automation.

79. SAST Tuning

The system should be optimised after the first scan.

For example:

custom sanitizer definitions,
false positive suppression,
framework models,
source/sink rules

can be added.

Good tuning significantly raises finding quality over time.

80. How Is Tool Accuracy Measured?

The total number of findings alone should not be the measure.

A more meaningful metric can be:

True Positive Rate

If 90 of 100 findings are false positives, for example, the tool can create operational problems.

The aim is not maximum findings but maximum meaningful signal.

81. The Human Factor in Source Code Analysis

However strong automation becomes, the specialist's experience matters.

Human analysis continues to be critical particularly in:

business logic,
complex authorisation,
architectural flaws,
attack chaining.

The modern model should therefore be thought of as:

Automation at Scale + Human Depth

82. The Analysis Team's Competence

It is not sufficient for the team performing source code analysis to know only cyber security.

Software development knowledge is needed too.

Knowledge of:

the programming language,
the framework,
design patterns,
databases,
API architecture

directly affects the quality of the analysis.

Because code security cannot be analysed without understanding the code.

83. The White Box Advantage

Source code analysis provides white box visibility.

Things the attacker cannot see from outside, such as:

internal validation,
security controls,
unreachable code,
sensitive functions

can be seen.

This is quite powerful when used together with a pentest.

84. How Are Source Code Analysis and Pentesting Used Together?

One of the ideal scenarios is:

SAST

↓

Manual Code Review

↓

Pentest

SAST provides broad code coverage.

Manual code review goes deeper on the critical points.

The pentest verifies the impact in the real running environment.

These three layers together create strong security visibility.

85. Source-Assisted Pentesting

The pentester having source code access can increase testing efficiency.

A hidden endpoint can be found from the code, for example.

The authorisation implementation can be understood.

Potential sinks can be seen.

Controlled testing can then be done at runtime.

This approach is valuable particularly for critical applications.

86. What Should the Purpose of the Report Be?

The purpose of a good source code analysis report is not to show how many findings the security team produced.

The purpose is to show the development team:

what to act on, why, and in what order.

The report must support decisions.

87. Should the Process End When the Report Is Delivered?

No.

The most valuable stage usually begins after the report.

Remediation,

the developer workshop,

the retest,

metrics

should continue.

Otherwise the report can lose its relevance a few months later.

88. Continuous Code Security

In mature organisations, source code analysis stops being a one-off service.

Automated controls run on every pull request.

Critical modules receive periodic manual review.

New CVEs are monitored continuously.

Pentests are done at major releases.

This structure can be approached from a Continuous Application Security perspective.

The SecureSys Source Code Analysis Methodology

At SecureSys we do not treat source code analysis as merely running a SAST tool and reporting its output.

To understand an application's real security level, we believe the analysis process must include the perspectives of:

scope, technology, architecture, business logic and attack surface

together.

Depending on project scope, the process can be handled as follows:

1. Scoping

Determining the repositories, branch, technologies and critical modules.

2. Architecture Analysis

Understanding the application's authentication, authorisation, data flow and integration structure.

3. Automated SAST Analysis

Broad scanning of the code base for security weaknesses.

4. SCA and Secret Scanning

Assessing third-party dependency and credential risks.

5. Manual Source Code Analysis

Expert examination of critical business logic, authentication, authorisation and sensitive transaction flows.

6. Security Triage

Filtering out false positive results and verifying the real risks.

7. CWE / OWASP / Risk Mapping

Standardising the findings.

8. Remediation Guidance

Providing development teams with applicable secure code recommendations.

9. Retest

Verifying the fixes from a security perspective.

The aim is not to produce as many findings as possible.

The aim is:

to find the real risks, explain them in a way the developer will understand, and create a structure that reduces the recurrence of the same security problems.

Because a good source code analysis does not only surface today's vulnerabilities.

It also contributes to the organisation producing more secure code tomorrow.

Frequently Asked Questions

How is source code analysis done?

Source code analysis generally consists of scoping, architecture review, automated SAST, manual code analysis, finding validation, risk rating, reporting and retesting.

Is the entire code base required for source code analysis?

It depends on scope. But access to the whole application, or to sufficient repository coverage, is useful in order to assess all data flows and framework behaviours correctly.

Can SAST output be used directly as a report?

It is not recommended in a professional assessment. Findings must be verified by a specialist in terms of false positives, reachability, business impact and real exploitation conditions.

Is manual review needed in source code analysis?

For critical applications in particular, yes. Areas such as business logic, authentication and authorisation benefit significantly from manual analysis.

How long does source code analysis take?

It varies according to the application's code size, technology diversity, architectural complexity and the scope of manual analysis.

Which programming languages can be analysed?

Depending on the SAST in use and the scope of expertise, Java, C#, JavaScript, TypeScript, Python, PHP, Go, C/C++, Kotlin, Swift and other languages can be analysed.

Should source code analysis and pentesting be done together?

It is quite a strong approach for critical applications. Source code analysis provides internal visibility, while a pentest assesses the running application from an attacker's perspective.

What should a source code analysis report contain?

It should contain the finding name, risk level, affected code, technical explanation, root cause, attack scenario, CWE/OWASP relationship, remediation and, where needed, a secure code example.