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Software evolution

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Software evolution is the term used in software engineering (specifically software maintenance) to refer to the process of developing software initially, then repeatedly updating it for various reasons.

General introduction

Fred Brooks, in his key book The Mythical Man-Month,[1] states that over 90% of the costs of a typical system arise in the maintenance phase, and that any successful piece of software will inevitably be maintained.

In fact, Agile methods stem from maintenance-like activities in and around web based technologies, where the bulk of the capability comes from frameworks and standards.[citation needed]

Software maintenance address bug fixes and minor enhancements and software evolution focus on adaptation and migration.

Impact

The aim of software evolution would be to implement (and revalidate) the possible major changes to the system without being able a priori to predict how user requirements will evolve.[2] The existing larger system is never complete and continues to evolve.[3] As it evolves, the complexity of the system will grow unless there is a better solution available to solve these issues. The main objectives of software evolution are ensuring the reliability and flexibility of the system. During the 20 years past, the lifespan of a system could be on average 6–10 years[citation needed]. However, it was recently[when?] found that a system should be evolved once every few months to ensure it is adapted to the real-world environment. This is due to the rapid growth of World Wide Web and Internet Resources that make it easier for users to find related information. The idea of software evolution leads to open source development as anybody could download the source codes and hence modify it. The positive impact in this case is large amounts of new ideas would be discovered and generated that aims the system to have better improvement in variety choices.

Changes in software evolution models and theories

Over time, software systems, programs as well as applications, continue to develop. These changes will require new laws and theories to be created and justified. Some models as well would require additional aspects in developing future programs. Innovations and improvements do increase unexpected form of software development. The maintenance issues also would probably change as to adapt to the evolution of the future software. Software process and development are an ongoing experience that has a never-ending cycle. After going through learning and refinements, it is always an arguable issue when it comes to matter of efficiency and effectiveness of the programs.[4]

Types of software maintenance

E.B. Swanson initially identified the three categories of maintenance: corrective, adaptive, and perfective. Four categories of software were then catalogued by Lientz and Swanson (1980).[5] These have since been updated and normalized internationally in the ISO/IEC 14764:2006:[6]

  • Corrective maintenance: Reactive modification of a software product performed after delivery to correct discovered problems;
  • Adaptive maintenance: Modification of a software product performed after delivery to keep a software product usable in a changed or changing environment;
  • Perfective maintenance: Modification of a software product after delivery to improve performance or maintainability;
  • Preventive maintenance: Modification of a software product after delivery to detect and correct latent faults in the software product before they become effective faults.

All of the preceding take place when there is a known requirement for change.

Although these categories were supplemented by many authors like Warren et al. (1999)[7] and Chapin (2001),[8] the ISO/IEC 14764:2006 international standard has kept the basic four categories.

More recently the description of software maintenance and evolution has been done using ontologies (Kitchenham et al. (1999),[9] Deridder (2002),[10] Vizcaíno (2003),[11] Dias (2003),[12] and Ruiz (2004)[13]), which enrich the description of the many evolution activities.

Stage model

Current trends and practices are projected forward using a new model of software evolution called the staged model [1]. Staged model was introduced to replace conventional analysis which is less suitable for modern software development is rapid changing due to its difficulties of hard to contribute in software evolution. There are five distinct stages contribute in simple staged model (Initial development, Evolution, Servicing, Phase-out, and Close-down).

  • According to K.H.Bennett and V.T Rajlich [1], the key contribution is to separate the 'maintenance' phase into an evolution stage followed by a servicing and phase out stages. The first version of software system which is lacking some features will be developed during initial development or also known as alpha stage [2]. However, the architecture has already been possessed during this stage will bring for any future changes or amendments. Most references in this stage will base on scenarios or case study. Knowledge has defined as another important outcome of initial development. Such knowledge including the knowledge of application domain, user requirements, business rules, policies, solutions, algorithm, etc. Knowledge also seems as the important factor for the subsequent phase of evolution.
  • Once the previous stage completed successfully (and must be completed successfully before entering next stage), the next stage would be evolution. Users tend to change their requirements as well as they prefer to see some improvements or changes. Due to this factor, the software industry is facing the challenges of rapid changes environment. Hence the goal of evolution is to adapt the application to the ever-changing user requirements and operating environment [1]. During the previous stage, the first version application created might contain a lot of faults, and those faults will be fixed during evolution stage based on more specified and accurate requirements due to the case study or scenarios.
  • The software will continuously evolve until it is no longer evolvable and then enter stage of servicing (also known as software maturity). During this stage, only minor changes will be done.
  • Next stage which is phase-out, there is no more servicing available for that particular software. However, the software still in production.
  • Lastly, close-down. The software use is disconnected or discontinue [2] and the users are directed towards a replacement.[1].
  • Very last, open-down

Lehman's Laws of Software Evolution

Main article: Lehman's laws of software evolution

Prof. Meir M. Lehman, who worked at Imperial College London from 1972 to 2002, and his colleagues have identified a set of behaviours in the evolution of proprietary software. These behaviours (or observations) are known as Lehman's Laws, and there are eight of them:

  1. (1974) "Continuing Change" — an E-type system must be continually adapted or it becomes progressively less satisfactory[14]
  2. (1974) "Increasing Complexity" — as an E-type system evolves, its complexity increases unless work is done to maintain or reduce it[14]
  3. (1980) "Self Regulation" — E-type system evolution processes are self-regulating with the distribution of product and process measures close to normal[14]
  4. (1978) "Conservation of Organisational Stability (invariant work rate)" - the average effective global activity rate in an evolving E-type system is invariant over the product's lifetime[14]
  5. (1978) "Conservation of Familiarity" — as an E-type system evolves, all associated with it, developers, sales personnel and users, for example, must maintain mastery of its content and behaviour to achieve satisfactory evolution. Excessive growth diminishes that mastery. Hence the average incremental growth remains invariant as the system evolves.[14]
  6. (1991) "Continuing Growth" — the functional content of an E-type system must be continually increased to maintain user satisfaction over its lifetime
  7. (1996) "Declining Quality" — the quality of an E-type system will appear to be declining unless it is rigorously maintained and adapted to operational environment changes
  8. (1996) "Feedback System" (first stated 1974, formalised as law 1996) — E-type evolution processes constitute multi-level, multi-loop, multi-agent feedback systems and must be treated as such to achieve significant improvement over any reasonable base[15]

It is worth mentioning that the applicability of all of these laws for all types of software systems has been studied by several researchers. For example, see a presentation by Nanjangud C Narendra[16] where he describes a case study of an enterprise Agile project in the light of Lehman’s laws of software evolution. Some empirical observations coming from the study of open source software development appear to challenge some of the laws [vague][citation needed].

The laws predict that the need for functional change in a software system is inevitable, and not a consequence of incomplete or incorrect analysis of requirements or bad programming. They state that there are limits to what a software development team can achieve in terms of safely implementing changes and new functionality.

Maturity Models specific to software evolution have been developed to improve processes, and help to ensure continuous rejuvenation of the software as it evolves iteratively[citation needed].

The "global process" that is made by the many stakeholders (e.g. developers, users, their managers) has many feedback loops. The evolution speed is a function of the feedback loop structure and other characteristics of the global system. Process simulation techniques, such as system dynamics can be useful in understanding and managing such global process.

Software evolution is not likely to be Darwinian, Lamarckian or Baldwinian, but an important phenomenon on its own. Given the increasing dependence on software at all levels of society and economy, the successful evolution of software is becoming increasingly critical. This is an important topic of research that hasn't received much attention.

The evolution of software, because of its rapid path in comparison to other man-made entities, was seen by Lehman as the "fruit fly" of the study of the evolution of artificial systems.

See also

Available tools

References

  1. ^ Fred Brooks, The Mythical Man-Month. Addison-Wesley, 1975 & 1995. ISBN 0-201-00650-2 & ISBN 0-201-83595-9.
  2. ^ Bennett, K. H.; Rajlich, V. T.; Mazrul,, R. Mohamad (1995). "Legacy System: Coping with success". IEEE Software. pp. 19–23.{{cite news}}: CS1 maint: extra punctuation (link)
  3. ^ Trung Hung Vo (2007), Software Maintenance
  4. ^ aeddy; ref: Understanding Open Source Software Evolution Walt Scacchi Institute for Software Research
  5. ^ Lientz, B.P. and Swanson, E.B., Software Maintenance Management, A Study Of The Maintenance Of Computer Application Software In 487 Data Processing Organizations. Addison-Wesley, Reading MA, 1980. ISBN 0-201-04205-3
  6. ^ ISO/IEC 14764:2006, 2006.
  7. ^ Paul Warren; Cornelia Boldyreff; Malcolm Munro (1999). "The evolution of websites". Proceedings of the Seventh International Workshop on Program Comprehension. IEEE. pp. 178–185. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  8. ^ Ned Chapin; Joanne E Hale; Khaled Md Khan; Juan F Ramil; Wui-Gee Tan (2001). "Types of software evolution and software maintenance". Journal of software maintenance and evolution: Research and Practice. 13 (1). Wiley Online Library: 3–30.
  9. ^ Barbara Kitchenham; Guilherme Travassos; Anneliese von Mayrhauser; Frank Niessink; Norman Schneidewind; Janice Singer; Shingo Takada; Risto Vehvilainen; Hongji Yang (1999). "Towards an ontology of software maintenance". Journal of Software Maintenance. 11 (6). Wiley: 365–389.
  10. ^ Dirk Deridder (2002). "A concept-oriented approach to support software maintenance and reuse activities". Proceedings of the 5th Joint Conference on Knowledge Based Software Engineering. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  11. ^ Aurora Vizcaíno; Jesús Favela; Mario Piattini (2003). "A multi-agent system for knowledge management in software maintenance". Knowledge-Based Intelligent Information and Engineering Systems. Springer. pp. 415–421. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  12. ^ Márcio Dias; Nicolas Anquetil; Káthia de Oliveira (2003). "Organizing the knowledge used in software maintenance". J. UCS. 9 (7): 641–658.
  13. ^ Francisco Ruiz; Aurora Vizcaíno; Mario Piattini; Félix García (2004). "An ontology for the management of software maintenance projects". International Journal of Software Engineering and Knowledge Engineering. 14 (03). World Scientific: 323–349.
  14. ^ a b c d e Lehman, M. M. (1980). "On Understanding Laws, Evolution, and Conservation in the Large-Program Life Cycle". Journal of Systems and Software. 1: 213–221. doi:10.1016/0164-1212(79)90022-0.
  15. ^ Lehman's laws of software evolution
  16. ^ Narendra, Nanjangud (29 April 2011). "Software Evolution in Agile Development". InfoQ. Retrieved 19 March 2016.

Further reading

  • Rajlich VT, Bennett KH, A Staged Model for the Software Life Cycle, Computer, July 2000, pp. 66 – 71
  • Andrea Capiluppi, Jesus M.Gonzalez Barahona, Israel Herraiz, Gregorio Robles, Adapting the "Staged Model for Software Evolution" to FLOSS
  • Mark C. Paulk, A History of the Capability Maturity Model Software
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