1 All you need is Quality
- 1.1 Blockchain
- 1.2 Static Confidence Factor
2 Quality Views
3 Anti-Patterns
4 McCabe®IQ Metrics
5 Halstead Metrics
6 OO Metrics
7 Quality Ranges

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, 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.

Quality Reviewer is part of Security Reviewer Suite.

The information collected, analysed and visualised with the SQALE methodology is easily comprehended and offers an incomparable insight into your software development. That facilitates the communication at all levels, from the IT directors to the developers and vice versa.

How can a technical manager communicate the positive effects of his or her work if the software quality remains largely invisible? How can budgeting departments, decision makers and internal customers be convinced of the necessity of the quality and productivity enhancing measures or the complexity of a particular work request?

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:

Each Severity has a different weight, named Defect Probability (DP). It is based on two decades of field experience, about correlation between code Quality and Defects in production.

The Violations (V) mean out-of-range Metrics as well as the number of Anti-Patterns found in the analyzed code, grouped in Static Defect Count (SDC).

For each Severity:

SDC(severity) = (V(severity)/NViol) * DP(severity)

where: V is the # of Violations per severity, and DP is the Defect Probability per severity

NViol= Total # of Violations

The Static Confidence Factor (SCF) is calculated:

SCF = (SDC(Blocker)+4)+(SDC(Critical)+2)+(SDC(Major)+2)+(SDC(Minor)+1)+(SDC(Info)+1)

Where: SDC is the Static Defect Count per severity.

Quality Views

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

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

You can create 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.

McCabe®IQ Metrics

McCabe® tab shows a complete list of McCabe® metrics, with Violations marked in different colors:

Halstead Metrics

Halstead Science metrics are also provided at Application/Program, File, Class and Method/Perform level, clicking on Halstead tab:

OO Metrics

The Chidamber & Kemerer (CK) metrics suite originally consists of 6 metrics calculated for each class: WMC, LCOM, CBO, DIT, RFC and NOC. A bunch of additional Object Oriented metrics are also calculated, like Mood, Cognitive Metrics and Computed Metrics. You can view them by clicking on OO Metrics tab:

Primitive Metrics: McCabe® Cyclomatic Complexity (vG), Essential Complexity (evG) Normal vG, sum vG, ivG, pvG, Cyclomatic Density, Design Density, Essential Density, Maintenance Severity, pctcom, pctPub, PUBDATA, PUBACCESS. SEI Maintanability Index (MI3, MI4), LOC, SLOC, LLOC. Halstead Length, Vocabulary, Difficulty, Effort, Errors, Testing Time, Predicted Length, Purity Ratio, Intelligent Content. OOPLOCM, Depth, Weighted Methods Complexity (WMC), LCOM, LCOM HS, CBO, DIT, RFC, NOA, NOC, NPM, FANIN, FANOUT, #Classes, #Methods, #Interfaces, #Abstract, #Abstractness, #DepOnChild.

Computed Metrics: let you define a new higher-level metric by specifying an arbitrary set of mathematical transformations to perform on a selection of Primitive metrics. A number of Computed Metrics are provided by default, like: Class Cohesion rate, Class Size (CS), Unweighted Class Size (UWCS), Specialization/Reuse Metrics, Logical Complexity Rate (TEVG), Class Complexity Rate (TWMC), Information Flow (Kafura & Henry), ISBSG Derived Metrics, Structure Complexity, Architectural Complexity Metrics, MVC Points (Gundappa).

Quality Ranges

You can configure Metrics ranges, having Low-Threshold-High values, you can set Alarm limits. It can be shown graphically. You can have System (Application/Program), File, Class and Method/Perform scope view, different for each supported programming language:

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.