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Instead of sticking to a lazy term, put some more effort to describe flaws of the code you see, and improvements you could think of.
Be specific on the flaws you see, even at the cost of being more verbose. “I find this function too complicated”. “This class has multiple responsibilities”. “The variable name does not describe its purpose clearly”.
Give a specific suggestion on how to improve the issue, if it not obvious from your description. Thins like “This class has multiple responsibilities. What would you think of breaking it into two parts, one being responsible for serialisation, another for price calculation?” or“I find the name calculate hard to understand. How about calling it getPriceWithoutVAT instead?”
Talk about the future implications of the code you see, if not changed. Things like “I think the class would probably be hard to reuse, because it has many things hardcoded.” or “I’m pretty sure newcomers would find this code hard to comprehend, as it does not follow conventions used across the codebase.”
Ask the person writing the code, what they think about your comments. Say thinks like “what do you think about this?” or “do you think this would make sense?”.
Even Uncle Bob has addressed the subject. In npm alone, countless packages have been released to provide an alternative. People tend to think that the catch with Contact and if-then-else is that the more branches in your code, the more opportunities for untested and unexpected behavior. Each branch requires different use cases to be thoroughly tested, and although some tools, such as Istanbul, provide an indication of how many branches have been performed, in addition to the traditional feature coverage. But branches are part of the nature of programming, there is nothing we can do about it. In this brief statement, I would like to give a new spin on when to use control structures and when to use some functional alternatives.
How do you define good quality software? How do you develop, measure, and ensure its quality? Read this post to learn the answer to these questions and more.
Code Quality Metrics: The Business Impact
The most effective Code Quality Metrics are those that help to track down the present flaws and ambiguities. The basic types of metrics for quality evaluation are:
Qualitative metrics
Quantitative metrics
Not amazingly, the qualitative estimations are more intuitive; quantitative options provide you with precise numbers to decide on the viability of your written crypto piece. So the qualitative techniques aid in categorizing the code as acceptable or rejectable, while the quantitative ones call for employing a formula as well as enter certain algorithms that exact the quality in terms of the levels of complexity.
The goal of every project is to generate an understandable and easily changeable codebase. Understandable writing is no less complex while staying appropriately formatted and documented. A changeable one, on the other hand, can be easily extended in the future. Therefore to grab an idea of the current levels of each of the quality issues leads to better results. In this case, the metrics of quality play a vital role in current evaluations and provide a track for further amendments.
Employing these techniques to excel in the performance of code directly impacts the profitability of the business. Achieving high-quality standards ultimately increases the ROI of the software. Consider it as a matter of choosing between investing excess time as well as resources initially or wasting the same later in fixing issues.
Qualitative Code Quality Metrics
1. Efficiency Metrics
The efficiency of a code is measured by the number of assets that are consumed to build a code. Time is taken to run the code also counts in the efficiency of the code. Ultimately a code that is efficient should come up to the requirements and specifications of the owner.
2. Extensibility Metrics
Software ought to be developed using changeable and extendable code. It should be extended for newer versions of the original program when incorporating advanced features without disturbing the overall program and software functions. Higher extensibility results in viability in code.
3. Well-documented
While documenting software, the programmer explains every single method and component along with the logic behind the various programming alternatives used. Reviewing such codes and assessing them gets less hectic than for those not properly listed. No doubt, the documentation part of the game plays a very important role in its quality assessment. It ensures that the program is readable as well as more maintainable for anyone who deals with it at any time. An undocumented code proves to be incomprehensible even by its developer sometimes.
4. Maintainability
The degree of ease in incorporating the alterations later on together with the prospects of malfunctioning of the whole application while making revisions counts for the maintainability characteristic.
The number of lines of the code within the application provides the figure to evaluate the maintainability. Less maintainability is inferred when these lines are more than the average number. Moreover, it's pretty obvious while we attempt to make alterations. The more the process is within the expected time frame; the higher is the level of maintainability.
5. Clarity:
A clear code is normally graded as the appreciated one. Most of the time, a single task of developing a code passes through various hands. Therefore, it must be understandable and comprehensible so that different engineers can easily read as well as work on it in various phases of development.
6. Readability and Code Formatting
Readability is more when your code is communicating what it ought to be. It uses the correct formatting, marks, and indentations. When the code is well oriented with the formatting requirements of the particular coding language, it's more logical and understandable and we say that it's more readable.
7. Testability Metrics
Programs higher on the testing metrics always result in better decision-making for future improvements by delivering exact information regarding future successful testing. High testability thus increases the efficiency of code by making the software working more reliable.
Quantitative Code Quality Metrics
1. Weighted Micro Function Points
One of the quantitative measures to use is WMFP. Just like scientific methods, WMFP, a sizing model that estimates employing mechanized measurements of a present original code by fragmenting it to smaller parts and generating numerous metrics displaying various levels of complexity. The findings are then tabulated into a numeric figure representing the rating. The result contains not only the mathematical computations but also the path of control, comments as well as code structures.
Weighted Micro Function Points Calculation.
2. Halstead Complexity Measures
Complexity accounts for the factors that contain several interrelated forming intricate code designs. This makes reading a code too difficult. Various parameters aid in finding out the readability and maintainability difficulties. The most famous one is Halstead's metrics.
Halstead complexity measures.
Halstead's metrics use indicators like the number of operators as well as operands to find out the complexity of the code in terms of errors, difficulty, efforts, size, vocabulary, and testing time. It views software as an execution of an algorithm with multiple symbols representing the operands or operators. The software is thus, a chronology of operators along with its linked operands that together provide the complexity estimate.
Halstead complexity measures example.
3. Cyclomatic Complexity
When joined with any size metric for example how many lines are there, this technique provides the marker of the testability and maintainability of the code. It employs decision-making parts within the program like switch-case, do-while, if-else to derive a graph.
It considers the underlying intricacy of the software by tallying the quantity of straightly autonomous lines across the program's original code. If the Cyclomatic finding is above 10, it means the quality needs to be corrected.
Cyclomatic complexity.
Documentez le code et l’architecture La documentation est parfois laissée de côté au moment du développement, par manque de temps ou de visibilité sur l'ensemble du projet. Elle est pourtant cruciale pour la maintenabilité de votre projet : elle permet de comprendre globalement le fonctionnement du code, et de savoir quelles parties du code sont affectées par une modification.
En programmation informatique, l' audit de code est une pratique consistant à parcourir le code source d'un logiciel afin de s'assurer du respect de règles précises. L'audit peut avoir un but légal (s'assurer que les licences des différentes parties d'un programme autorisent bien à le distribuer tel quel, ou voir l'audit interne de ReactOS), ou s'assurer de la sécurité du logiciel en testant sa vulnérabilité, ou bien chercher des bugs.
Un audit de code source est une analyse poussée du code source d'une application, afin de déterminer si la manière dont elle est développée répond aux standards désirés. Cet audit peut être réalisé de manière automatisée et/ou manuelle, afin de découler sur des actions correctives et un plan progrès.
Sonar s'appuie sur 7 types de métriques différentes qui sont présentées grâce à l'image suivante : - Architecture & design : ce critère désigne tout ce qui est en rapport avec l'architecture telle que les différentes dépendances entre les classes
- Duplications : ce critère désigne tout ce qui se rapporte à la duplication de code au sein du même fichier ou dans plusieurs fichiers
- Unit tests : ce critère se rapporte aux tests unitaires comme le nombre de tests réussis ou échoués mais il prend également en compte les différentes parties du code couvertes ou non par les tests
- Complexity : ce critère désigne la complexité cyclomatique moyenne par classe, fichier et méthode
- Potential bugs : ce critère désigne les différentes failles de sécurité ou bogues qui pourraient être présent dans les sources
- Coding rules : ce critère désigne les règles de codage comme le nom des attributs ou des classes
- Comments : ce critère désigne tout ce qui concerne les commentaires, du commentaire vide jusqu'au commentaire de documentation en passant par les lignes de codes commentées
Mob programming can support teams in changing old habits into new effective habits for creating products in an agile way. Collectively-developed habits are hard to forget when you have other people around. Mob programming forces individuals to put new habits into practice regularly, making them easier to adopt. Teams are intolerant of repetition, and are always looking for better ways of doing their work.
Chris Lucian, the director of software development at Hunter Industries, spoke about improving technical quality with mob programming and collective habits at Agile 2021.
Improvement in habits came naturally with mob programming, as Lucian explained:
While working with multiple people at the same computer at the same time, you have a sort of accountability group. Naturally, you start to eliminate bad habits and instill good ones simply because the feedback loop is constantly available.
Nombreux sont les témoignages de développeurs qui rapportent que travailler sur d’anciens projets avec une base de code large s’avère généralement cauchemardesque dans la mesure où il faut d’abord comprendre le fonctionnement du code écrit par des tiers, mais également être capable de comprendre les bogues qui surviennent et appliquer les solutions idoines. Ce même sentiment est partagé par de nombreux développeurs qui travaillent sur de nouveaux projets dont la base de code est très étendue. Face aux difficultés rencontrées par les développeurs qui doivent à chaque fois se représenter une carte mentale de tout le code sur lequel ils travaillent pour ne pas être perdus dans ses méandres, CodeSee tente d’apporter une solution avec Review Maps, un outil intégré à sa plateforme de production et conçu pour donner en temps réel une carte détaillée de tout le code du projet afin que les développeurs puissent se concentrer sur les aspects techniques du code et non sur sa compréhension générale.
Justin Gottschlich, scientifique principal et directeur/fondateur de Machine Programming Research à Intel Labs a annoncé le 20 octobre que ControlFlag, l'outil de débogage automatisé d'Intel, est désormais open source.
Points Clés - Le code est toujours testable en identifiant les anti-modèles et en les corrigeant.
- La testabilité de la conception et du code affecte la capacité à automatiser les tests.
- Les décisions de conception sont prises par les développeurs, et les testeurs peuvent les influencer pour une meilleure testabilité.
- Les pratiques de code propre et la testabilité vont de pair afin que les développeurs et les testeurs puissent en bénéficier.
- Les discussions conjointes en cours entre les développeurs et les testeurs peuvent aider à améliorer la testabilité,
- Les chefs d'équipe et les managers doivent favoriser les discussions conjointes dans le cadre des processus d'amélioration.
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Dans cet article, il sera démontré comment simplifier le code applicatif en exploitant les possibilités du SGBD grâce aux contraintes.
This is my summary of the The Pragmatic Programmer, by Andrew Hunt and David Thomas. I use it while learning and as quick reference. It is not intended to be an standalone substitution of the book so if you really want to learn the concepts here presented, buy and read the book and use this repository as a reference and guide.
Here are some of the patterns that an organization can follow when applying test automation:
Pyramid Testing
When following the testing pyramid, there will be three automated test levels coming in before you get to the manual tests.
Automated Unit Tests
This level would be the starting point at the bottom of the pyramid. It involves a lot of test situations. These would be done separately, which shortens the execution time per block of the application code.
You get to see how the software behaves under different circumstances, try valid and invalid inputs, and also discover any unexpected behavior. Here, you can get a lot of feedback quickly.
Automated Integration Tests
In this next level, you try scenarios that involve different code components. The focus is on how these components work together and whether every call and response is spot on.
You also won’t have to run many test scenarios. At this level, things may move a bit slower since you’re mainly examining the intercommunication aspect. These test scenarios would cover APIs, methods, classes, etc.
Automated UI Tests
This would be the third level, and would also be of an end-to-end nature. You test an application with the integrations running and emulate real-life user interactions. This level would have tests running much slower since you’re testing more elaborate scenarios from start to end.
You’ll have fewer tests, covering major features, happy paths, and more. You get to discover how several components work together when needed in a typical use case.
The Ice Cream Cone (Anti-Pattern)
ice cream cone pattern test
In this pattern, the testing pyramid is inverted, so to speak. This means that a lot more of the QA team’s efforts are sucked away from automating unit tests. The same will happen for the other testing levels, though it might be to a lesser extent.
Nevertheless, the result is most of the testing is being done manually. Automated tests will largely be at UI/end-to-end tests level, and to some extent, the integration tests too. The result is a team that is stretched thin with these upper test levels. You’ll have fewer automated unit tests and move much slower in this aspect.
You may also have bugs and other random issues constantly trickling down from the manual tests, but not always being caught on time. In an agile environment, demanding frequent releases, you can’t thoroughly respond and make the necessary changes when unit tests are limited. You won’t be able to strike a decent balance between delivery speed and remainder flaws on your next release.
The Cupcake (Anti-Pattern)
Here, there’s a team for development, manual testing, and automated testing. They all work separately and each team tries to cover as many scenarios at each test level as possible. The problem is, with minimal communication, a lot of time can be wasted as teams have massive overlap in the scenarios tested.
And while there might be more automated UI and integration tests, unit tests still lag behind and there are more hands on deck for manual testing.
How to Measure Code Quality?
The question on the minds of many experienced software developers is, "How can I improve my code quality?"
There are many different ways of measuring code quality. They range from traditional inspection of the code's structure to more modern methods like code translation or high-level languages like Java.
Code Correctness and Code Readability
In web development, code quality refers to two separate but related ideas: code correctness and code readability.
Good code quality ensures that clients can easily understand the code; low-quality code may lead to unexpected results, incorrect behavior, and security vulnerabilities. It is why comprehensive web code review and testing are essential for every web application development project.
Good quality code communicates every bit of its intent to developers and customers clearly and brings down the risk of misinterpretation and mistakes.
Code Reliability
It's also imperative that it meets technical debt requirements. If you've spent weeks or months following a specific design, then you're well aware of how it interacts with existing code.
Code Re-Usability
It means that the same application can be used in different environments, and this will decrease program delays and technical debt.
Code Maintainability
Maintainability measures how easily the application is changed and maintains the existing functionality.
Sujet de mémoire : Audit technique de code
L' audit informatique (en anglais Information Technology Audit ou IT Audit) a pour objectif d'identifier et d'évaluer les risques (opérationnels, financiers, de réputation notamment) associés aux activités informatiques d'une entreprise ou d'une administration.
Coincé avec un code cassé? Impossible d'identifier la raison du bogue? Il est temps d'analyser votre code pour les problèmes causés! Logiciels et applications Web
Il y a beaucoup d’outils open source qui peuvent aider à identifier les mauvaises pratiques de codage.
Dans le monde Java, trois outils d’analyses statiques ont résisté à l’épreuve du temps, et sont largement utilisés de manière très complémentaire. Checkstyle excelle dans la vérification des conventions et normes de codage, les pratiques de codage, ainsi que d’autres mesures telles que la complexité du code. PMD est un outil d’analyse statique similaire à Checkstyle, plus focalisé sur les pratiques de codage et de conception. Et FindBugs est un outil innovant, issu des travaux de recherche de Bill Pugh et de son équipe de l’université du Maryland, qui se focalise sur l’identification du code dangereux et bogué. Et si vous être en train de travailler avec Groovy ou Grails, vous pouvez utiliser CodeNarc, qui vérifie la norme et les pratiques de codage de Groovy.
Tous ces outils peuvent être facilement intégrés dans votre processus de build. Dans les sections suivantes, nous verrons comment configurer ces outils pour générer des rapports XML que Jenkins peut ensuite utiliser dans ses propres rapports.
The untested code gap kills productivity and predictability. We don’t choose where the bugs go making them easy to find; they choose their hiding places, and have no mercy on us. They flare up and put teams into fire fighting mode.
Are we are beat? Do we have to put up with buggy products and long test and fix cycles? No, we do not! We must accept that a product test strategy based on manual test is unsustainable. Unsustainable systems eventually collapse. We can’t afford not to automate much of your software test.
We should write tests to enable developers to move fast with confidence. Code is always evolving, so question everything, collect experience, and judge for yourself.
"One difference between a smart programmer and a professional programmer is that the professional understands that clarity is king. Professionals use their powers for good and write code that others can understand." - Clean Code: A Handbook of Agile Software Craftsmanship by Robert C Martin.
Google s’invite aujourd’hui dans le débat et montre qu’en réalité les commentaires peuvent très souvent être évités. « En lisant un code, souvent, il n'y a rien de plus utile qu'un commentaire bien placé. Cependant, les commentaires ne sont pas toujours bons. Parfois, le besoin d'un commentaire peut être un signe que le code devrait être refactorisé. Utilisez un commentaire lorsqu'il est impossible de faire en sorte que votre code s'explique par lui-même », expliquent Dori Reuveni et Kevin Bourrillion sur Google Testing Blog.
Si vous pensez avoir besoin d'un commentaire pour expliquer ce qu'est un code, ils proposent alors de procéder d'abord à l'une des opérations suivantes
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