Software engineering requires constant efforts from the entire team to succeed. With the industry’s tremendous competition, all software engineering teams are working rigorously to outperform their competitors. How do you accurately measure and improve your software engineering team’s productivity in such a dynamic and competitive environment? There are many ways of increasing the productivity of engineering teams without increasing the number of employees or the number of hours in the workday. This blog post provides the techniques and models available to measure the productivity of software engineering teams.
Planning provides a direct link between business and technology and enables you to utilize technology for proper business value addition.
With proper planning, every team member knows exactly what they are accountable for throughout the project’s duration. So, before you start working, plan the project strategy and approach that will lead you to reach the end goal. Take the time to understand which development tools you’ll use during the process and identify the technical design that will support your software.
A smooth workflow helps to give every team member clear task and goal to complete while also removing needless tasks and interruptions. Physically planning out your workflow on a noticeable board to the whole team helps the team maintain focus and increases the enthusiastic software engineering team’s results.
Make sure you have all of the following:
A sprint goal describes what the Software engineering team plans to accomplish during the sprint. It is a high-level summary of the goal the product owner would like to achieve. A sprint goal basically describes the purpose of a sprint and states why the sprint was undertaken in the first place. Working together on a clear and focused Sprint goal promotes teamwork and positively impacts trust and morale.
Software engineering teams must work with one shared goal to ensure that everyone is heading in the same direction. Once a particular goal has been selected, the team implements it and checks whether the goal has been met or not at the end of the sprint. For example, let’s say you want to learn if users are willing to register as the first step in the user journey. To make this possible, use the usability test or product demo at the end of the sprint to validate if you have met the goal. It will also help you understand if it’s OK to ask people to register first or if this creates a barrier to adoption.
Commitment to the sprint goal will help your team:
Code quality impacts the overall software quality, and this quality determines how safe, secure, and reliable your codebase is. Code quality simply refers to those features that cater to customers’ needs and subsequently provide product satisfaction. It is an excellent indicator of quality, and with an in-house team, one can easily ensure the code quality is maintained. Moreover, with the rapid acceptance of outsourcing models, here are a few indicators for an offshore scrum development team.
The first and foremost measure of quality to consider is whether the code meets the requirements that caused it to be written. Every code must actually meet the customer’s needs. Therefore, when a code fails to accomplish the desired outcome (even if the code is beautifully written), it is said to have low quality.
How To Measure and Improve Code Quality?
Some key code Quality Aspects to Measure are:
This measures the probability that a system will run without failure over a specific period of operation. Running a static analysis tool is used to measure the number of defects. Software availability can also be calculated using the mean time between failures (MTBF).
Measures how easily software can be maintained in relation to the size, complexity, consistency, and codebase structure. Halstead complexity measures and the number of stylistic warnings and can be used to improve maintainability. Both automation and human reviewers are crucial for developing maintainable codebases.
It helps to measure how well the software supports testing efforts. That is how well you can control, isolate, observe, and automate testing. Cyclomatic complexity can help you improve the testability of the component.
It is usually used to measure how usable the same software is in different environments. Enforcing a coding standard can help with portability.
This measures whether existing assets (like code) can be used again. The number of interdependencies can measure it, and a static analyzer can help you identify these interdependencies.
By measuring code quality, you can quickly understand the necessary steps that need to be taken to improve your code quality. Numerous metrics can be employed to quantify the quality of your code. Here are some of the metrics to help you achieve high code value:
Regular Code Reviews. These reviews enable the software engineering teams to collaborate and share knowledge, improves their work, and ensure that code adheres to established standards.
Functional Testing. Encourages software development teams to focus on software functionality from the outset and reduce extraneous code.
Clear Requirements. A project with clear, feasible requirements is more likely to achieve high quality than ambiguous, poorly specified requirements.
Coding Standard. Using a coding standard ensures high-quality code, improves consistency and readability of the codebase. The best way to use a coding standard is by using a static code analyser.
Measuring impacts and resolutions is a continuous process that involves defining, collecting, and analysing data on the software development process and its products to understand and control the process and its products. It also helps to supply meaningful information to improve that process and its products. No software engineering team can build quality software, or improve a process, without measurement. Measuring impacts and resolutions is significant in achieving the primary management objectives of prediction, progress, and process improvement.
Presently, software measurement has become a primary aspect of good software development practice. With adequate measurement activities, any software development team adds value and is actively involved in, and informed of, all phases of the development process. Measurement also enables us to make specific characteristics of our processes and products more visible. If everything is not measurable, we should make an effort to make it measurable.
It is no longer news that many software faults are caused by violated dependencies not recognized by the software engineering team while designing and implementing a software system. The failure to acknowledge these dependencies could be a result of the technical properties of the dependencies themselves. It can also be from the way the development work is organized.
Excessive inter-module dependencies have long been recognized as an indicator of poor software design. Highly coupled systems, where the modules have unnecessary dependencies, are usually difficult to work with. The reason is that modules are hardly understood in isolation, while extensions or changes to functionality can’t be contained. In managing software systems using dependencies, the software engineering team should ensure that dependencies are extracted from the code by conventional static analysis and shown in a tabular form known as the ‘Dependency Structure Matrix’ (DSM).
Several algorithms are readily available and can assist in organizing the matrix by using a type of form that shows the highlights patterns, architecture, and problematic dependencies. A hierarchical data structure that is partially obtained by such algorithms and input from the user now becomes the idea for ‘design rules’ that expresses the architect’s motive about which dependencies are acceptable.
Customer involvement in software engineering is vital for building successful software products. Incremental improvements and enhancements of software require an in-depth and continuous understanding of customer needs. Also, the engineering team needs to ensure that the mechanisms for managing customer feedback data need to be in place. However, research shows that the feedback loops from customers are slow, and obtaining timely feedback is challenging. The customer-centric requirement helps a team thoroughly investigate customer feedback mechanisms and how customer data can inform continuous improvement of software products.
To do this, companies need to create transparent relationships with customers both during and after product development and deployment. Companies also need to improve data-driven development practices to increase the accuracy of their product management (PM) decisions and research and development (R&D) investments.
The minimum viable product (MVP) is that part of a new product which allows a software engineering team to collect the maximum amount of validated learning and information about customers with the least effort.
An MVP for software development projects should:
One of the most important reasons why you want to define and opt for MVP development process in the first place is to consider a release timeline. This may be dependent on various factors such as fixed availability dates, business opportunities, or just making sure you are ahead of the curve. The delivery timeline available to develop the product will impact how much you will be able to deliver.
Another reason to define the scope of the MVP and delivery timelines is so that you can know what can wait for future product versions.
It is always said that software maintenance claims a large portion of organizational resources and consumes most of the costs of a software system during its entire life. Software maintenance is the total activity required to provide cost-effective support to a software system. These activities are performed both during the pre-delivery stage and post-delivery stages.
The four main aspects that software maintenance focuses on include:
According to the ISO/IEC 14764, these are the types of maintenance that should be carried out by a software engineering team:
Corrective maintenance: It is a reactive modification of a software product performed after delivery to correct discovered problems.
Adaptive maintenance: Modification of a software product that is performed after delivery to keep a software product usable in a changed or changing environment.
Perfective maintenance: This involves modifying a software product to improve its performance or maintainability after delivery.
Preventive maintenance: This involves modifying a software product after delivery in order to expose and correct hidden faults in the product before they progress to bigger faults.
As we’ve seen already, measuring the productivity of your software engineering team comes with lots of benefits. While many managers will tout the merits of managerial oversight as a primary detractor to not hiring a remote team, studies have increasingly proved that by employing a remote team, you can easily hire the best talent from anywhere in the world and save on the recruitment cost.