Software intelligence

Software Intelligence is insight into software structure 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] Similarly to Business Intelligence (BI), Software Intelligence is produced by a set of software tools and techniques for the mining of data and software inner-structure. End results are information used by business and software stakeholders to make informed decisions, communicate about software health, measure the efficiency of software development organizations, and prevent software catastrophes.[2]

Components

Because of the complexity and wide range of components implied in software, Software intelligence is made up of an increasing number of components including:

  • Graphical visualization of the inner structure of the software product or application considered[3]
  • Code analyzer to serve as an information basis for other components
  • Dependency representation, from data acquisition (automated and real-time data capture, end-user entries), up to data storage
  • Navigation capabilities within components and impact analysis features
  • Grades or scores of the structural and software quality aligned with industry-standard like OMG, CISQ or SEI
  • List of architectural and coding violations against standardized best practices[4]
  • Reporting structural alert
  • Industry references and benchmarking allowing comparisons between outputs of analysis and industry standards

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, this was mainly related to SAS activities, in which he has been an expert since 1979.

In 1983, Victor R. Basili detailed a methodology for collecting valid software engineering data relating to software engineering, evaluation of software development and variations with initial goals in November 1984 in IEEE Transactions on Software Engineering journal.[5]

In 2004, the ISV CAST is the first commercial company to broadly use and copyright the term as part of the name of their software intelligence product, the CAST Application Intelligence Platform, a software analysis, and measurement product [6].

Lately, 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.[7]

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.[8]

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.[9]

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:

  • Comprehensive: missing information may lead to a wrong or inappropriate decision, as well as it is a factor influencing the user acceptance of a system.[10]
  • Accurate: accuracy depends on how the data is collected to ensure fair and indisputable opinion and judgment.[11]
  • Precise: precision is usually judged by comparing several measurements from the same or different sources.[12]
  • Scalable: lack of scalability in the software industry is a critical factor leading to failure.[13]
  • Credible: outputs must be trusted and believed.
  • Deploy-able and usable

Applications

Software intelligence has many applications in all businesses relating to the software environment, whether it is software for professionals, individuals, or embedded software.

  • Providing analytics about the software itself or stakeholders involved in the development of the software, e.g. productivity measurement to inform business and IT leaders about progress towards business goals.[14]
  • Assessment and Benchmarking to help business and IT leaders to make informed, fact-based decision about software.[15]
  • Measuring against industry standards to diagnose structural flaws in an IT environment.[16]
  • Compliance validation regarding security, specific regulations or technical matters.
  • Uniform documentation on all inner components of the software.[17]

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:

  • Application Portfolio Analysis (APA) aiming at improving the enterprise performance[18][19]
  • Software Assessment for producing software KPI[20]and improve quality and productivity
  • Software security and resiliency measures and validation
  • Software evolution or legacy modernization, for which blueprinting the software systems are needed nor tools improving and facilitating modifications

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. ^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
  3. ^Renato Novais, José Amancio Santos, Manoel Mendonça, Experimentally assessing the combination of multiple visualization strategies for software evolution analysis, Journal of Systems and Software, Volume 128, 2017, pp. 56–71, ISSN 0164-1212, doi:10.1016/j.jss.2017.03.006.
  4. ^Software Engineering Rules on code quality. http://it-cisq.org/standards/code-quality-standards/
  5. ^Victor R. Basili and David M. Weiss. 1984. A Methodology for Collecting Valid Software Engineering Data. IEEE Trans. Softw. Eng. 10, 6 (November 1984), 728–738. doi:10.1109/TSE.1984.5010301
  6. ^Kamen, Yuru; Alur, Deepak; Ali, Sied M.; Crupi, John P.; Malks, Daniel B. (August 2010). “Method and apparatus for Cooronating State and Execution Context of Interpreted Languages” (PDF). United State Patent. CAST Application Intelligence Platform Empowering Application Management’; CAST the Application Intelligence Company; Oct.2004;
  7. ^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. doi:10.1145/1882362.1882397
  8. ^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.
  9. ^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.
  10. ^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
  11. ^Lippert, S.K., & Forman, H. (2005). Utilization of information technology: examining cognitive and experiential factors of post-adoption behavior. IEEE Transactions on Engineering Management, 52, 363–381.
  12. ^Rajiv D. Banker and Chris F. Kemerer (1992). Performance Evaluation Metrics for Information Systems Development: A Principal-Agent Model. Information Systems Research, volume 3, number 4, 379–400.
  13. ^ Crowne, “Why software product startups fail and what to do about it. Evolution of software product development in startup companies,” IEEE International Engineering Management Conference, 2002, pp. 338–343 vol.1. doi:10.1109/IEMC.2002.1038454
  14. ^LaValle S, Lesser E, Shockley R, Hopkins MS and Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Management Review 52 (2), 21–32.
  15. ^Janez Prašnikar, Žiga Debeljak,Aleš Ahčan (2005) Benchmarking as a tool of strategic management, Total Quality Management & Business Excellence, volume 16, number 2, 257–275, doi:10.1080/14783360500054400
  16. ^https://www.bcg.com/capabilities/technology-digital/software-intelligence.aspx
  17. ^Parnas, David Lorge (2011), Precise Documentation: The Key to Better Software,The Future of Software Engineering, 125–148, doi:10.1007/978-3-642-15187-3_8
  18. ^https://www.gartner.com/it-glossary/application-portfolio-analysis
  19. ^https://www.gartner.com/doc/3812067/effective-strategies-deliver-sustainable-cost
  20. ^https://www.omg.org/spec/AFP

 

Ofer Abarbanel online library