Software engineering tasks leave lots of data in its trail including project plans, requirement specifications, design documents, source code, test plans, defect data, log files, ticket data and so on. Locked inside this data is the information about
dynamics of software development, quality of software, best practices, defect resolutions, reasons for build failures etc. Using various AI techniques such as machine learning, deep learning, natural language processing (NLP), information visualization
etc it is possible to guide the software engineering professionals with AI enabled decision making and automations.
AI enabled tools for software engineering is an evolving market and has players from different background. A vibrant start-up ecosystem involved in this space are brining in innovations and also witnessing lot of merger and acquisitions. Big IT
product firms like Microsoft, IBM, Adobe are enhancing their own offerings to include AI e.g. IBM is enriching its Rational suite with AI use cases, Microsoft is incorporating AI features in Visual studio, Adobe is leveraging AI for its design related
software XD etc. Software engineering tool vendors are also not behind in enriching their own offerings with AI e.g. Atlassian, Planview etc. Large IT service companies, on the other hand, are leveraging their diverse IT project delivery experience
and building set of offerings for solving problems of their clients. Sopra Steria offers a comprehensive set of offerings through its grounds-up R&D effort or leveraging its ecosystem of universities, partners and start-ups.
This series of articles provide an overview of some of the use cases adopted by industry. This list will continue to evolve as many more use cases are in research phase in academia, start-ups and R&D labs of large companies.
Management is about achieving desired objectives under given constraints. The success of a manager depends on his(her) experience, instincts and attention to details. AI can clearly help as it mimics the experience by learning from prior projects, both
successful and not so, and thus provide right instincts to face an unknown new situation. In fact, Gartner anticipates that AI will eliminate 80% of today’s manual project management tasks by 2030, and as indicated by examples given below, this
does not seem far from reality.
- A Nordic start-up, Forecast, has put together a platform which leverages AI to learn from thousands of projects and use them to help make informed decisions for new projects. Using AI enabled features it can predict project delivery dates, forecast
capacity needs, perform project cost estimations etc. The Auto Schedule feature of the Forecast platform uses the historical experience to assign tasks, create schedule, sets deadline and optimize resources for a given project. After a few months
with the platform, it can learn enough about the people and their completion rates that it can start predicting about the unique team members the project has. Visualizations such as heat maps can be used to ensure that no body is overloaded and
capacities are utilized intelligently.
- Aptage, an Austin Texas based start-up, acquired by Planview in Jun 2020, applies AI and ML in the area of portfolio management and work management. It leverages visualizations to monitor risks over time, provides AI assisted recommendations and simulations
and helps managers know when the project is heading off track before its too late.
Established project management tools have started to integrate AI either through alliances or leveraging their own R&D efforts. Atlassian’s JIRA Service Desk 3.1.x incorporates machine learning capabilities for providing the smart search, smart
insights and smart actions to the users. Digital.ai versionOne platform, formerly CollabNet VersionOne, can integrate with Parasoft tool suite for its deep code analysis capabilities.
Lot of activities related to voice and vision in management (as well as subsequent) phases can be automated using AI. Asana provides voice and vision related capabilities to improve meeting productivity. It has features like creating actionable tasks
and sub tasks from whiteboard notes of a brainstorming session etc. AI powered tools like Rawshort can help by converting text directly into video.
- Researchers at the University of Wollongong, Deakin University, Monash University and Kyushu University have developed a framework, , which can be used to build a smart, AI-powered agile project management assistant. The researchers proposed a new
framework for the use of AI technologies, within the context of agile project management. The capabilities envisioned could help product owners identify product backlog items (e.g. user stories and tasks), refine them (e.g. decomposing an epic
into a number of user stories, splitting user stories into small stories, and breaking a user story into a number of tasks), and detect duplicates and dependencies. It could also help agile teams in sprint planning, for instance, by selecting
items in the product backlog for the upcoming sprint, recommending optimal sprint plans, or predicting future risks and mitigations. The prototype of the solution is in progress and researchers are actively looking for industrial partners to realize
the complete vision laid out in the paper.
Although we are very far from autonomous, or even prescriptive, project and risk management but industry has made long strides in implementing AI in management use cases.
Sopra Steria has also invested in creating a data lake based solution which can help our clients take more informed management decisions pertaining to costing, staffing and so on. More details of the solution will be provided in the last edition of this
In the following articles we will see the AI led software engineering use cases in subsequent project lifecycles phases and also Sopra Steria’s offerings in this space.
Check out the 1st part
AI led Software Engineering
CIOs are expected to partner business, and at times leads, the delivery of digital transformation. The existing IT landscape of a company needs to be rationalized and modernized to be able to achieve the expected business velocity.