All you need is Quality

Quality Reviewer evaluates regressions and understands changes in the source code using automated Software Metrics visualization (SW complexity, size and structure Metrics, Halstead Metrics, ISO 9126 maintainability, ISO 25010, ISO 19515, Chidamber & Kemerer, SQALE), as well as Effort Estimation (APPW, AFP, QSM FP, SRM FP, COSMIC, COCOMO, Revic) and reporting features. It helps to keep code entropy under control, be it in house development or outsourced maintenance projects.

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Quality Reviewer makes a significant contribution in this area. SQALE enhanced reporting feature provide an overview of your entire software landscape which even non-technical individuals can understand easily. Managers and decision makers can see evidence of the quality of the system and the productivity increases achieved and can therefore be more easily convinced of measures such as software quality assurance. In reverse, developers and team leaders can show managers and directors what they have achieved.

Blockchain

Blockchain is a meta technology on the internet that works as a decentralized database thanks to a peer to peer network of computers and people which share a distributed ledger.

It consists of data structure blocks—which hold exclusively data in initial blockchain implementations, and both data and programs in some of the more recent implementations—with each block holding batches of individual transactions and the results of any blockchain executables. Each block contains a timestamp and information linking it to a previous block.

A transaction represents a unit of value somebody has and that is willing to exchange for something (physical or not) with somebody else. This unit of value will go from owner A to owner B by broadcasting to the network that the amount on your account goes down and the amount on the other person goes up. How do nodes in the network keep track of account balances? Ownership of funds is verified through links to previous transactions, which are called inputs.

Quality Reviewer can share the results in anonymous way using Blockchain, under permission of the User. 

Existing electronic Quality systems, like QSM and ISBSG, all suffer from a serious design flaw: they are proprietary, that is, centralized by design, meaning there is a single supplier that controls the code base, the database, and the system outputs and supplies the browsing tools at the same time. The lack of an open-source, independently verifiable output makes it difficult for such centralized systems to acquire the trustworthiness required by enterprises and quality standard makers. The blockchain works as a secure transaction database, to log the audit quality results in a trustworthy way. The results are classified by Industry, Application Types and Size.

The software metrics data available on Blockchain can be used to assist you with:

• Estimation

• Benchmarking

• Infrastructure planning

• Bid planning

• Outsourcing management

• Standards compliance

• Budget support

Static Confidence Factor

The Static Confidence Factor is a measurement standard combining the most important Quality Analysis results in a single value. It is calculated by collecting 20 Quality Metrics and 20 Anti-Patterns, classified in 5 Severity Levels. The lower is the Static Confidence Factor, the higher is the Application Quality. From Static Confidence Factor derives the Quality Index, both provided by Quality Reviewer. Example of Quality Index:

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          Where: SDC is the Static Defect Count per severity.

Application Architecture

The Application Architecture gives you a roadmap and best practices to follow when building an application, so that you end up with a well-structured app. Quality Reviewer is able to create a Design Structure Matrix (DSM) and a Call Graph, by reading the source code, binaries and/or related UML diagrams.

DSM-Design Structure Matrix

An example of Quality Reviewer’s automatically generated Design Structure Matrix is shown below:

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A Design Structure Matrix consists of a matrix to visualize Dependencies of hierarchically organized elements and a set of algorithms which can be applied on the matrix to sort the elements in order to discover layering.

A Design Structure Matrix (DSM) is made by two parts

• A matrix to visualize dependencies

• Algorithms which can be applied on the matrix to discover e.g. layering in the software.

The DSM consists of a matrix with the same elements in the rows and columns

• The hierarchy of packages and elements is visible on the left.

• The relations between the elements are shown in the cells.

In a DSM, the hierarchy can also be folded in whole or in part. The relational strengths of the collapsed cells are simply combined. As a result, the DSM will become more compact, but will still remain correct in terms of content.

In this way it is possible to display a system with thousands of elements and still keep the overview.

DSM Architectural Discovery

By applying a partitioning algorithm on the DSM, the layering of the software will be discovered by Quality Reviewer during the Static Analysys. Such an algorithm tries to reorder the DSM in such a way that as many relationships as possible come under the diagonal.

After partioning elements with many inbound relationships (providers) have shifted to the bottom, while the elements with many outgoing relationships (consumers) have shifted to the top.

Cyclic relations can be easily spotted, because the type of relations remain above the diagonal.

DSM Assist in refactoring

A DSM can be used to improve the dependency structure. One can think of:

• Removing cyclic dependencies.

• Improving the cohesiveness of a component by move elements to other component were the have stronger relations.

In the matrix we can move an element to another component or layer, combine it with other elements or split and then recalculate all dependencies to see if this yields a better dependency structure.

After recalculating the dependencies it can be seen that the cyclical relationship between the presentation and application layer has disappeared.

The advantages of such an impact analysis are particularly evident in improvement scenarios that take place at the architectural level and thus affect multiple components. Without the use of a DSM, such analysis are unreliable, because the intended and the actual software architecture often do not match.

DSM-Supported Languages

• .NET: C# and vb.NET Assembly

• JAVA, Clojure, Groovy, Kotlin JAR files

• C/C++ Source and Header files

• UML in Sparx System Enterprise Architect format

• DSM Files from 3rd-party tools (for example ArgoUML)

• DSI Model. Two DSI Model files can be compared

A DSI Model file is a way to import 3rd-party analyzer’s DSM output to Quality Reviewer.. A DSI file represent an analyzer’s output. Each code analyzer must export its DSM results to DSI file. To ensure that the Quality Reviewer can import this file, it must conform the DSI file XSD schema below:

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Call Graph

Starting from source code, a Call Graph is automatically generated by Quality Reviewer showing how classes and functions call each other within an application:

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Each oval represents a function. Each arrow indicates a function call. In the diagram above, the main program is represented by node MAIN.
It calls 6 functions, one of which calls 9 other functions.

It parses source code for function definitions and calls, generates a call graph image, and displays it on screen.
Supported languages for Call Graph are:
bash, go, lua, javascript, typescript, julia, kotlin, perl, php, python, R, ruby, rust, scala, swift.

For JAVA, C and C++ the call graph is quite different:

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Reading those call graphs, you can easily understand the software Architecture, as well as the Application’s Module Dependencies.

Quality Views

Further, in a single view, you can have a summary of Quality Violations for the entire Project:

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McCabe® IQ-style Kiviat graph can help to show where your Quality issues are mainly located (Maintanability, Testability or Size). For each source file, all related Classes or Programs are listed. 

Anti-Patterns

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You can create Anti-patterns are common solutions to ineffective problems and cause more problems than they solve. Anti-patterns are the opposite of best practice, which is a solution that has been proven to be effective. They are often used because they seem to work, but the larger context or the long-term consequences are often not considered. They can occur in Software Development, Architecture Design and Project Management.

Available Anti-Patterns

Software Development Anti-Patterns

• Accidental Complexity

• Blob Class

• Code Review Candidate

• Complex Class

• Excessive Coupling

• Excessive Overloading

• Indecent Exposure

• Large Class

• Lava Flow

• Long Method Class

• Long Parameter List

• Poltergeists

• Speculative Generality

Architecture Anti-Patterns

• Bloated Service

• Dead Component

• Dead Element

• Deficient Encapsulation

• Deficient Names

• Documentation

• Duplication

• Functional Decomposition

• Lasagna Code

• Lazy Component (Class)

• Refactoring Candidates

• Spaghetti Code

• Swiss Army Knife

Further to the above available Software Development and Architecture Anti-Patterns, you can create your own custom Anti-Patterns based on metrics’ search queries, using graphs to interpret the impact of the values. When metrics based searches provide quick access to elements of interest, saving these queries serve as input for custom analysis.

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Supported Programming Languages: C#, Vb.NET, VB6, ASP, ASPX, JAVA, JSP, JavaScript, TypeScript, Java Server Faces, Ruby, Python, R, GO, Clojure, Kotlin, eScript, Apex, Shell, PowerShell, LUA, HTML5, XML, XPath, C, C++, PHP, SCALA, Rust, IBM Stream Programming Language, Objective-C, Objective-C++, SWIFT, COBOL, ABAP, SAP-HANA, PL/SQL, T/SQL, Teradata SQL, SAS-SQL, ANSI SQL, IBM DB2, IBM Informix, MySQL, FireBird, PostGreSQL, SQLite.COPYRIGHT (C) 2014-2022 SECURITY REVIEWER SRL. ALL RIGHTS RESERVED.