What are the best software quality metrics to prioritize technical debt?

I recommend always using "low coupling" and "high cohesion". This solves part of the problem for the complexity of the code to be implemented. Being simple often solves the problem. And thinking about not implementing everything we have in mind, knowing that there is no such thing as perfect software, but that it must be "good enough". Technical debts will always exist, you just shouldn't focus on them all the time; but they must be paid.

Cyclomatic complexity < 4 Naming methods based on the purpose. - When code is short and the method and variable names make sense, anyone can easily understand what the code is doing. - Make your methods and variables names clear, so others (or even yourself) can quickly figure out what's happening in the code. - Keeping code brief and using names that make sense is like writing a code manual – it helps everyone understand what's going on without extra effort.

Code Complexity Metrics: (1) Cyclomatic Complexity: Measures the complexity of a program based on the number of independent paths through the code. Higher complexity may indicate increased difficulty in maintaining and understanding the code. (2) Maintainability Index: Combines various metrics, including cyclomatic complexity, to provide an overall measure of code maintainability. Lower maintainability index values suggest higher technical debt

Organizar e padronizar o código s?o passos essenciais para enfrentar a complexidade. Na minha experiência como desenvolvedor, pude concluir que estruturas claras e normas consistentes aceleram a compreens?o. A legibilidade do código é algo fundamental, e sempre digo que, um código bem escrito é um código que até uma crian?a consegue ler e entender o que aquilo faz, e isso n?o apenas facilita a colabora??o, mas também otimiza a manuten??o do software.

Code complexity often arises from non optimal choices made around data structures and abstractions within the application domain. The challenge lies in selecting the right data structures for efficient information handling and creating abstractions that accurately represent the underlying logic.

Code coverage is very important, but the experience of the team involved will determine the success of its implementation, and it is the team's responsibility to establish how to implement it. I recommend using Cypress, it's also excellent tool.

Quando você tem uma alta cobertura, isso significa que grande parte do seu código está sendo verificada por testes automáticos, o que é um ótimo sinal. Isso pode ser um indicativo de que você está no caminho certo para manter a dívida técnica sob controle, prevenindo erros de regress?o em futuras mudan?as. Pra tornar simples eu gosto de trabalhar com mapas mentais e modelos gráficos que mostram com facilidade até que nível chega a cobertura de código, e anteciparmos cenários futuros. Esses mapas retratam a última execu??o de testes e seus status + detalhes.

While code coverage serves as an interesting metric in software development, it's crucial for teams to establish metrics tailored to their specific context, especially in the case of legacy systems. Legacy codebases often pose challenges for achieving comprehensive coverage. It's essential to recognize that a high code coverage percentage doesn't automatically equate to good code quality. The effectiveness of tests plays a crucial role; well-written tests that align with the application's logic and functionality are far more valuable than merely achieving a high coverage percentage.

On my first job, my boss told me this one thing: "Before you push on production, make sure that you were able to break the system". Code coverage is very important as it can also bring forth some of the vulnerabilities of the system. Most of the time, I prefer the manual way of testing the system, as I get to be in the user's skin and it makes it easier to also do automated tests and know where to improve especially in terms of code efficiency.

In my opinion code coverage is very bad quality metric, as most of the time it is artificially inflated thus hiding real status of codebase. If you see large codebase with near 100% code coverage be VERY careful ;)

Code tool at the time of Code review help us understand if these issues are related to a person or for the org. Having the issue at the org reqquire more rigour of code reviews and code check ins. Having these embedded and run automatically help us to identify these issue if there are fracture in code review process or due to emergency code delivery. Normally no body would like to have code smell knowingly so this have to be handled at one step higher.

My recommendation before using the tools is to think before implementation; I like to say that using paper and pencil (or board and pen) are some of the most effective methods. The team should establish clear rules. And the usual code review is also crucial.

Its a culture thing driven by personalities. Keep it simple 1. By investing in good tools like Coverity, Sonar for static analysis in the pipeline 2. Have extensive TDD & BDD so that checkins dont create regression in actual code flow of feature, even if quality static analysis is done. 3. Last have a strategy to run continous load, capacity, longevity test

Code smells are indications of deeper design issues found in the code. large or complicated functions, repeated blocks, large parameter lists, incorrect modularization, and other issues show where technical debt is being developed. Code smell monitoring reveals areas that require reworking for readability, extensibility, and simplicity. The severity of the code smells and the related functionality's business effect define the cleanup priority. These are recognizable by tools for static analysis and automated linting. Resolving code smells enhances maintainability and has long-term benefits.

This require our code review to be more diligent and if required have multiple code reviewers to avoid leak in the system. Fixing Linting , code smell and working on build a good review process help us to block such tech debt int he long run as these short fracture in code will slow become large and larger as we lose discipline around code management.

Examining code problems that are highlighted by tools gives insight into the accumulation of technical debt that must eventually be paid off. Any warning or problem that arises during builds is a result of coding discipline gaps; these include unsafe type conversions, hardcoded constants, missing headers, and unneeded imports. Over time, problems multiply, making reform challenging. Tracking trends in code problem severity and density drives technical debt prioritization for components genuinely harming developer productivity or application quality. To stop problems, establish code standards, and make enforcement possible through automated checks.

Embracing linters from day one is a strategic move in software development, significantly contributing to the overall code quality. Linters play a pivotal role in identifying and rectifying potential issues, enforcing coding standards, and enhancing code consistency.

The good thing about this is we have now multiple AI tools like Git Copilot, Blackbox AI and others that can also help you detect some of the issues in your code.

Identificar problemas de código é apenas o primeiro passo. Ao utilizar métricas, como a taxa de defeitos por linha de código, podemos quantificar o impacto real. Essas abordagens numéricas oferecem uma vis?o objetiva, fazendo com que seja possível fazer uma fila de prioridade de corre??es com eficiência, direcionando esfor?os para áreas mais críticas.

Evite dívida técnica estabelecendo práticas sólidas e revis?es frequentes. Refatore gradualmente áreas críticas, introduza automa??o de testes e promova comunica??o efetiva. Adote revis?es proativas para identificar problemas precocemente. Em casos de alta rotatividade, intervenha rapidamente com corre??es direcionadas, ajustando procedimentos e envolvendo a equipe na busca da solu??o.

Uma alta rotatividade do código pode indicar que um arquivo está sofrendo muitas mudan?as para corrigir bugs, implementar novas funcionalidades ou adaptar-se a requisitos voláteis. Isso pode sinalizar que o arquivo contém elementos que n?o est?o relacionados entre si, uma alta complexidade ou uma má modulariza??o, que s?o exemplos de dívidas técnicas. Por outro lado, uma baixa rotatividade do código pode indicar que um arquivo está obsoleto, n?o é usado ou n?o é testado. Isso pode sinalizar que o arquivo tem uma baixa relevancia, uma alta dependência ou uma má documenta??o, que também s?o exemplos de dívidas técnicas.

Urgency and Impact: - Urgency: If the debt causes frequent incidences, prioritize it. If it doesn't or perhaps nobody uses it, you can leave it be for now - Impact: if every time it causes trouble, you lose a day to fix it, it has a higher impact than one with a simple workaround

Code Duplication Percentage: Identifies the percentage of duplicated code in the codebase. High duplication levels can lead to increased maintenance efforts and are indicative of technical debt.

Addressing code problems that lead to bugs is a essential aspect of software development, and minimizing these issues should be a top priority. The number of bugs directly correlates with the quality of the user experience, making it the most crucial metric to tackle from the outset. Frequently, an increased incidence of bugs indicates gaps in the team's QA process, highlighting the importance of comprehensive testing. A thorough QA process is essential for identifying and rectifying potential issues across various scenarios, ensuring a smoother user experience and a more robust software product.

I also see flakieness in tests as an indicator for urgent action. Flaky tests increase the time your changes spend unmerged and unreleased. Another such indicator, that something is wrong are long test execution times.

It’s really quite simple. Just answer these questions: 1. Is it easy to change? 2. Is it easy to understand? 3. Does it rely on outdated dependencies? 4. Does it use deprecated code? That’s it.