Software intelligence

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Software Intelligence is insight into the inner workings and structural condition of software assets produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems in information technology environments. [1] [2] Similarly to business intelligence (BI), software intelligence is produced by a set of software tools and techniques for the mining of data and the software's inner-structure. Results are automatically produced and feed a knowledge base containing technical documentation and blueprints of the innerworking of applications, [3] and make it available to all to be used by business and software stakeholders to make informed decisions, [4] measure the efficiency of software development organizations, communicate about the software health, prevent software catastrophes. [5]

Contents

History

Software intelligence has been used by Kirk Paul Lafler, an American engineer, entrepreneur, and consultant, and founder of Software Intelligence Corporation in 1979. At that time, it was mainly related to SAS activities, in which he has been an expert since 1979. [6]

In the early 1980s, Victor R. Basili participated in different papers detailing a methodology for collecting valid software engineering data relating to software engineering, evaluation of software development, and variations. [7] [8] In 2004, different software vendors in software analysis start using the terms as part of their product naming and marketing strategy.

Then in 2010, Ahmed E. Hassan and Tao Xie defined software intelligence as a "practice offering software practitioners up-to-date and pertinent information to support their daily decision-making processes and Software Intelligence should support decision-making processes throughout the lifetime of a software system". They go on by defining software intelligence as a "strong impact on modern software practice" for the upcoming decades. [9]

Capabilities

Because of the complexity and wide range of components and subjects implied in software, software intelligence is derived from different aspects of software:

Components

The capabilities of software intelligence platforms include an increasing number of components:

User Aspect

Some considerations must be made in order to successfully integrate the usage of software Intelligence systems in a company. Ultimately the software intelligence system must be accepted and utilized by the users in order for it to add value to the organization. If the system does not add value to the users' mission, they simply don't use it as stated by M. Storey in 2003. [20]

At the code level and system representation, software intelligence systems must provide a different level of abstractions: an abstract view for designing, explaining and documenting and a detailed view for understanding and analyzing the software system. [21]

At the governance level, the user acceptance for software intelligence covers different areas related to the inner functioning of the system as well as the output of the system. It encompasses these requirements:

Applications

Software intelligence has many applications in all businesses relating to the software environment, whether it is software for professionals, individuals, or embedded software. Depending on the association and the usage of the components, applications will relate to:

Marketplace

The software intelligence is a high-level discipline and has been gradually growing covering applications listed above. There are several markets driving the need for it:

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References

  1. ↑ Dąbrowski R. (2012) On Architecture Warehouses and Software Intelligence. In: Kim T., Lee Y., Fang W. (eds) Future Generation Information Technology. FGIT 2012. Lecture Notes in Computer Science, vol 7709. Springer, Berlin, Heidelberg
  2. ↑ Hinchey, Mike; Jain, Amit; Kaushik, Manju; Misra, Sanjay (Jan 2023). "Guest Editorial: Intelligence for systems and software engineering". Innovations in Systems and Software Engineering. Springer. 19 (1): 1–4. doi:10.1007/s11334-023-00526-1. PMC   9886201 . PMID   36744022.
  3. ↑ Bartoszuk, C., Dąbrowski, R., Stencel, K., & Timoszuk, G. "On quick comprehension and assessment of software.", In Proceedings of the 14th International Conference on Computer Systems and Technologies, June 2013, pp. 161-168 doi : 10.1145/2516775.2516806
  4. ↑ Raymond PL Buse, and Thomas Zimmermann. "Information needs for software development analytics." 2012 34th International Conference on Software Engineering (ICSE). IEEE, June 2012, pp. 987-996 doi : 10.1109/ICSE.2012.6227122
  5. ↑ Ahmed E. Hassan and Tao Xie. 2010. Software intelligence: the future of mining software engineering data. In Proceedings of the FSE/SDP workshop on Future of software engineering research (FoSER '10). ACM, New York, NY, USA, 161–166
  6. ↑ "Mr. Kirk Paul Lafler". 21 December 2015.
  7. ↑ Basili, Victor R. (1981). Data collection, validation and analysis. Software Metrics: An Analysis and Evaluation (PDF). MIT Press. p. 143. ISBN   0-262-16083-8.
  8. ↑ Basili, Victor R.; Weiss, David M. (Nov 1984). "A Methodology for Collecting Valid Software Engineering Data". IEEE Transactions on Software Engineering. IEEE Trans. Softw. Eng. 10, 6 (November 1984) (6): 728–738. doi:10.1109/TSE.1984.5010301. hdl: 1903/7513 .
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  10. ↑ Nierstrasz, Oscar, and Theo Dirk Meijler. "Research directions in software composition." ACM Computing Surveys 27.2 (1995): 262-264 doi : 10.1145/210376.210389
  11. ↑ Kanashiro, L., et al. "Predicting software flaws with low complexity models based on static analysis data." Journal of Information Systems Engineering & Management 3.2 (2018): 17 doi : 10.20897/jisem.201817
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  16. ↑ "Software Engineering Rules on code quality". Object Management Group, Inc. 2023. Retrieved 15 December 2023.
  17. ↑ Balalaie, Armin, , Abbas Heydarnoori, and Pooyan Jamshidi. "Microservices architecture enables devops: Migration to a cloud-native architecture." Ieee Software 33.3 ,May–June 2016, 42-52, doi : 10.1109/MS.2016.64
  18. ↑ Q. Feng, R. Kazman, Y. Cai, R. Mo and L. Xiao, "Towards an Architecture-Centric Approach to Security Analysis," 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA), Venice, 2016, pp. 221-230, doi : 10.1109/WICSA.2016.41
  19. ↑ R. Haas, R. Niedermayr and E. Juergens, "Teamscale: Tackle Technical Debt and Control the Quality of Your Software," 2019 IEEE/ACM International Conference on Technical Debt (TechDebt), Montreal, QC, Canada, 2019, pp. 55-56, doi : 10.1109/TechDebt.2019.00016
  20. ↑ Storey MA. (2003) Designing a Software Exploration Tool Using a Cognitive Framework. In: Zhang K. (eds) Software Visualization. The Springer International Series in Engineering and Computer Science, vol 734. Springer, Boston, MA.
  21. ↑ Seonah Lee, Sungwon Kang, What situational information would help developers when using a graphical code recommender?, Journal of Systems and Software, Volume 117, 2016, pp. 199–217, ISSN   0164-1212, doi : 10.1016/j.jss.2016.02.050.
  22. ↑ Linda G. Wallace, Steven D. Sheetz, The adoption of software measures: A technology acceptance model (TAM) perspective, Information & Management, Volume 51, Issue 2, 2014, pp. 249–259, ISSN   0378-7206, doi : 10.1016/j.im.2013.12.003
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  25. ↑ Crowne, M. (9 July 2003). "Why software product startups fail and what to do about it. Evolution of software product development in startup companies". IEEE International Engineering Management Conference. pp. 338–343. doi:10.1109/IEMC.2002.1038454 . Retrieved 8 December 2023.
  26. ↑ Parnas, David Lorge, Precise Documentation: The Key to Better Software, The Future of Software Engineering, 2011, 125–148, doi : 10.1007/978-3-642-15187-3_8
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