Automated Text Analysis And Learning Tools Discover Tech Debt Systematically
The software developers of toady use several automated tools and techniques for proper analysis of technical debt, discover the issues more systematically than before. Such practices followed by several software developing companies to stay ahead in the competition and prevent any possible chance of early strangulation of the company. To explore, a search is run on a sample set of issues and examined to find the words like custom, duplicate, workaround, legacy, hacky, refactor, rewrite, refresh and cleanup. With such search results, they could find a significant difference statistically between the percentage of the issue with any one of such keyword and considered tech debt with those sets of issues which are not classified as tech debt.
Assessing Debt Accumulation
The software developers find assessing debt accumulation to be the most challenging and difficult part when it comes to classifying tech debt issues systematically. There are primarily two reasons for such difficulty in the process. The first problem is the language used by the developers to describe debt accumulation is different and less explicit than the language used to describe the issues related to the designing of the code. Such unstructured language in indicating accumulation of debt results in a misunderstanding of the fact for the reviewers to consistently identify an item that is responsible in debt accumulation.
Types Of Accumulation
It also causes enough difficulty for the developers to assess the impact of it on the project along with the researchers who studies how to automate classification of tech debt. There are three different types in which such accumulation are informed which also leads to a problem. The information depends on the accumulation that already exists with relation to the problem in question or the future potentiality of accumulation recurring and accumulation that is related to the potential solution regarding the current problem in hand. Limiting the scope of accumulation to any one type during the process is therefore required for researching and fixing the problem.
Future Research Opportunities
Several future research opportunities crop up from such findings, and plans are made to evaluate other methods to find out the underlying unstructured data in the software repositories for locating tech debt. Tracking of tech debt in the text discussion of the developer to code through commit log so that evaluation efficacy of self-reported debt can be done in the issue trackers. Effective tech debt management techniques and model dimensions of such accumulation about the cost of fixing and not fixing along with the influence of time can be done to improve management guidelines.
Build On The Investment
Therefore, building on the investment to conduct correlated studies with vulnerabilities of software with the defects would enable better understanding and quantify of tech debt. Just like your knowledge about how to consolidate credit card debt helps you to clear your multiple debts, use of decision flow description would also help you to take properly formulated and strategically planned decisions to discover, analyze multiple tech debt items to pay off as well as manage tech debt in your project more systematically.