Why Do We Need Software Engineering?

To understand the necessity for software engineering, we must pause briefly to look back at the recent history of computing. This history will help us to understand the problems that started to become obvious in the late sixties and early seventies, and the solutions that have led to the creation of the field of software engineering. These problems were referred to by some as “The software Crisis,” so named for the symptoms of the problem. The situation might also been called “The Complexity Barrier,” so named for the primary cause of the problems. Some refer to the software crisis in the past tense. The crisis is far from over, but thanks to the development of many new techniques that are now included under the title of software engineering, we have made and are continuing to make progress.

In the early days of computing the primary concern was with building or acquiring the hardware. Software was almost expected to take care of itself. The consensus held that “hardware” is “hard” to change, while “software” is “soft,” or easy to change. According, most people in the industry carefully planned hardware development but gave considerably less forethought to the software. If the software didn’t work, they believed, it would be easy enough to change it until it did work. In that case, why make the effort to plan?

The cost of software amounted to such a small fraction of the cost of the hardware that no one considered it very important to manage its development. Everyone, however, saw the importance of producing programs that were efficient and ran fast because this saved time on the expensive hardware. People time was assumed to save machine time. Making the people process efficient received little priority.

This approach proved satisfactory in the early days of computing, when the software was simple. However, as computing matured, programs became more complex and projects grew larger whereas programs had since been routinely specified, written, operated, and maintained all by the same person, programs began to be developed by teams of programmers to meet someone else’s expectations.

Individual effort gave way to team effort. Communication and coordination which once went on within the head of one person had to occur between the heads of many persons, making the whole process very much more complicated. As a result, communication, management, planning and documentation became critical.

Consider this analogy: a carpenter might work alone to build a simple house for himself or herself without more than a general concept of a plan. He or she could work things out or make adjustments as the work progressed. That’s how early programs were written. But if the home is more elaborate, or if it is built for someone else, the carpenter has to plan more carefully how the house is to be built. Plans need to be reviewed with the future owner before construction starts. And if the house is to be built by many carpenters, the whole project certainly has to be planned before work starts so that as one carpenter builds one part of the house, another is not building the other side of a different house. Scheduling becomes a key element so that cement contractors pour the basement walls before the carpenters start the framing. As the house becomes more complex and more people’s work has to be coordinated, blueprints and management plans are required.

As programs became more complex, the early methods used to make blueprints (flowcharts) were no longer satisfactory to represent this greater complexity. And thus it became difficult for one person who needed a program written to convey to another person, the programmer, just what was wanted, or for programmers to convey to each other what they were doing. In fact, without better methods of representation it became difficult for even one programmer to keep track of what he or she is doing.

The times required to write programs and their costs began to exceed to all estimates. It was not unusual for systems to cost more than twice what had been estimated and to take weeks, months or years longer than expected to complete. The systems turned over to the client frequently did not work correctly because the money or time had run out before the programs could be made to work as originally intended. Or the program was so complex that every attempt to fix a problem produced more problems than it fixed. As clients finally saw what they were getting, they often changed their minds about what they wanted. At least one very large military software systems project costing several hundred million dollars was abandoned because it could never be made to work properly.

The quality of programs also became a big concern. As computers and their programs were used for more vital tasks, like monitoring life support equipment, program quality took on new meaning. Since we had increased our dependency on computers and in many cases could no longer get along without them, we discovered how important it is that they work correctly.

Making a change within a complex program turned out to be very expensive. Often even to get the program to do something slightly different was so hard that it was easier to throw out the old program and start over. This, of course, was costly. Part of the evolution in the software engineering approach was learning to develop systems that are built well enough the first time so that simple changes can be made easily.

At the same time, hardware was growing ever less expensive. Tubes were replaced by transistors and transistors were replaced by integrated circuits until micro computers costing less than three thousand dollars have become several million dollars. As an indication of how fast change was occurring, the cost of a given amount of computing decreases by one half every two years. Given this realignment, the times and costs to develop the software were no longer so small, compared to the hardware, that they could be ignored.

As the cost of hardware plummeted, software continued to be written by humans, whose wages were rising. The savings from productivity improvements in software development from the use of assemblers, compilers, and data base management systems did not proceed as rapidly as the savings in hardware costs. Indeed, today software costs not only can no longer be ignored, they have become larger than the hardware costs. Some current developments, such as nonprocedural (fourth generation) languages and the use of artificial intelligence (fifth generation), show promise of increasing software development productivity, but we are only beginning to see their potential.

Another problem was that in the past programs were often before it was fully understood what the program needed to do. Once the program had been written, the client began to express dissatisfaction. And if the client is dissatisfied, ultimately the producer, too, was unhappy. As time went by software developers learned to lay out with paper and pencil exactly what they intended to do before starting. Then they could review the plans with the client to see if they met the client’s expectations. It is simpler and less expensive to make changes to this paper-and-pencil version than to make them after the system has been built. Using good planning makes it less likely that changes will have to be made once the program is finished.

Unfortunately, until several years ago no good method of representation existed to describe satisfactorily systems as complex as those that are being developed today. The only good representation of what the product will look like was the finished product itself. Developers could not show clients what they were planning. And clients could not see whether what the software was what they wanted until it was finally built. Then it was too expensive to change.

Again, consider the analogy of building construction. An architect can draw a floor plan. The client can usually gain some understanding of what the architect has planned and give feed back as to whether it is appropriate. Floor plans are reasonably easy for the layperson to understand because most people are familiar with the drawings representing geometrical objects. The architect and the client share common concepts about space and geometry. But the software engineer must represent for the client a system involving logic and information processing. Since they do not already have a language of common concepts, the software engineer must teach a new language to the client before they can communicate.

Moreover, it is important that this language be simple so it can be learned quickly.

Are We Moving Towards Service-Oriented Software Engineering?

The digitally disrupted and the technology-driven world calls for quicker solutions that don’t compromise on quality. For organisations, software engineering services that come with the requisite agility, proven methodologies, and thoroughness are required for reduced turnaround time and better ROI.

It is time to adopt the service-oriented software engineering services in order to get the best of both worlds i.e. software engineering and cloud computing. You will, in turn, be able to improve quality and time taken to launch the software applications while integrating the database from legacy systems. The incredible combination of services and cloud computing has attracted many large scale businesses and applications due to several advantages: easy development, smooth outline for mission-critical applications, and a cost-effective journey from simple to complex applications. Another concern that large enterprises have is security, which is also taken care of through secure choice of clouds.

While service-oriented software engineering and cloud technology solutions are similar in matters such as resource outsourcing and IT management, they differ in some ways. Service-based software engineering services concentrate wholly on architecture design using service composition and discovery while, cloud computing focuses on the essential delivery of the services, which means the SOA for the two differ.

The architectural dimension for service computing

The architectural model for service computing works for the development and deployment concepts. When you define service, it is individual and independent for a particular software entity and comes with well-defined standards and functions. These individual services are then combined to form a workflow based on the application needs. Software as a Service is when the software is self-contained and platform independent. Instead of the software, you can have the platform as your service, where each service that comes into contact to form the workflow is dependent on the platform.

Organisations deploy their applications using a well-defined SOA which is based on the development and deployment service computing chosen by the organisation. The SLA defines the service and the terms of usage and the service provider, in this case, will need to adhere to these terms.

The benefit of service based software development services would be increased agility, defined processes, and quicker time to market. With cloud technology solutions taking over, it is important to define the service computing standards so that you can maximise the data security, and harness the potential of your data. You can compile the services, search, discover and even test and execute the services individually or as a workflow anytime, thus reducing the whole time to develop, debug and deploy.

Characteristics of services computing

The different characteristics of services computing that you need to be aware of before opting for the same include:
· Loosely coupled: No dependency exists between the different services
· Abstract: The logic stays hidden within the SLA
· Reusable: The components can be reused
· Composable: A single service comprises various other services, which can help developers work together and build a single service workflow with ease.

With such amazing architectural and structural benefits, software engineering services are indeed the future of software engineering in the age of cloud computing.

Dagstuhl Seminar End – User Software Engineering

I have been reading this abstracts paper from the Dagstuhl Seminar End-User Software Engineering and it has lots of interesting points to make.

In the Dagstuhl Seminar report (Burnett et al) it is stated that “The number of end users creating software is far larger than the number of professional programmers. These end users are using various languages and programming systems to create software in forms such as spreadsheets, dynamic web applications, and scientific simulations. This software needs to be sufficiently dependable, but substantial evidence suggests that it is not.” This point relates to that of (Ko) (Carnegie Mellon University) who explains that the goals of end-users may be unrelated to production of code, but instead they are interested in their domain problem, this means they perceive programming barriers as distractions. Ko explains that end-user programmers must be allowed to focus on their goals, and an important part of the solution is to visualise the whole program execution not just the output. A further problem outlined by Ko is that of programs which were intended to be temporary and owned by a particular person becoming central to a company, this often happens with spreadsheets.

(Henry Lieberman) of MIT Media Laboratory asks “Why is it so much harder to program a computer than simply to use a computer application? I can’t think of any good reason why this is so; we just happen to have a tradition of arcane programming languages and mystically complex software development techniques. We can do much better.” He argues that researchers should use program transformation, and visualisation to make the end-user programming process as automatic as possible. In order that people can become End-User Software Engineers without their even realizing it. This needs to involve interdisciplinary research to combine different research approaches. (Blackwell) also argues the need for interdisciplinary research on the end-user programming problem to identify techniques within software engineering that can assist with this problem.

(Coutaz) explains how Model Driven Engineering and Service Oriented Architecture can be combined. Coutaz also explains that “An interactive system is a graph of models related by mappings and transformations.” This would fit in well with the structure of RDF (Resource Description Framework) a Semantic Web standard, which is also a graph structure. Alexander Repenning of University of Lugano explains the need for enhancements to UML (Unified Modeling Language) to aid end-user programming. (Engels) of University of Paderborn also explains that UML should be extended to allow development of user interfaces in order to assist end-users to program. (Repenning) also argues that “Visual programming languages using drag and drop mechanisms as programming approach make it virtually impossible to create syntactic errors.” So “With the syntactic challenge being – more or less – out of the way we can focus on the semantic level of end-user programming.” (Rosson) of Pennsylvania State University also explains about creation of a web based drag and drop interface. Abraham and (Erwig) of Oregon State University integrate spreadsheet modelling into the UML modelling process. (Gerhard Fischer) of University of Colorado explains the concept of meta-design as aimed at creating infrastructures for collaborative design assuming future uses and problems cannot be completely anticipated during development of a system. (Dittrich) of University of Copenhagen argues that more research is needed into the software lifecycle and methods and tools needed for end-user developers, especially when they are collaborating. These end-users often need to adjust old software for new purposes. (Costabile and Piccinno) also explain that new methodologies and environments are required for supporting end-user collaborative development

(De Souza) argues that the goal of human-computer interaction (HCI) will evolve from making systems easy to use to making systems that are easy to develop. Lieberman also argues that HCI experts have concentrated on ease of use and should examine ease of programming. (Begel) of Microsoft Research explains that if programming is left only to programmers rather than allowing domain experts to be involved the program becomes a black box and the domain expert cannot trust or verify the results. He explains that text based computer languages are often too obscure for end-user programmers. Begel also explains that end-users may lack an engineering mindset to form mental models of how to make the computer do what they want. Segal of the Open University argues that professional engineers have a history of programming so can be distinguished from other end-user programmers as having less problems with coding. (Fischer) explains that it is the mismatches between end-users needs and software support that enables new understandings. Fischer also argues that software development can never be completely delegated to software professionals because domain experts are the only people that fully understand the domain specific tasks that must be performed. He also argues for an approach to enabling end-user programming that makes it interesting to end-users. He explains that often the problem is that end-users find programming boring rather than that they find it hard. (Spahn et al) explains that end-users of ERP (Enterprise Resource Planning) systems are domain experts not IT professionals, and this is forcing them to communicate their needs to IT developers. Spahn et al argue for the empowerment of users to customise software by providing an abstraction layer to hide technical details and allow for concentrating on business needs.

(Clarke) of Microsoft examines the characteristics of end-user developers in order to assist with meeting their needs. (Fischer) hypothesises that this emphasis on end-user development also changes the emphasis on testing “Software testing is conducted differently. Because domain expert developers themselves are the primary users, complete testing is not as important as in the case when the developers are not the users.”

References

End-User Software Engineering – Dagstuhl Seminar – Summary – http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=2007081 – PDF Abstracts and links to papers – http://eusesconsortium.org/docs/dagstuhl_2007.pdf – Margaret M. Burnett, Gregor Engels, Brad A. Myers and Gregg Rothermel – From 18.01.07 to 23.02.07, the Dagstuhl Seminar 07081 End-User Software Engineering was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed.

Abraham, R., Erwig, M., 2007. Exploiting Domain-Specific Structures For End-User Programming Support Tools. In: End-User Software Engineering Dagstuhl Seminar.

Begel, A., 2007. End User Programming for Scientists: Modeling Complex Systems. In: End-User Software Engineering Dagstuhl Seminar.

Blackwell, A., 2007. Interdisciplinary Design Research for End-User Software Engineering. In: End-User Software Engineering Dagstuhl Seminar.

Burnett, M. M., Engels, G, Myers, B. A., Rothermel, G., 2007. End-User Software Engineering Dagstuhl Seminar.

Clarke, S., 2007. What is an End User Software Engineer?. In: End-User Software Engineering Dagstuhl Seminar.

Costabile, M. F., Piccinno, A., 2007. Software environments for supporting End-User Development. In: End-User Software Engineering Dagstuhl Seminar.

Coutaz, J., 2007. Meta-User Interfaces for Ambient Spaces: Can Model-Driven-Engineering Help?. In: End-User Software Engineering Dagstuhl Seminar.

De Souza, C., 2007. Designers Need End-User Software Engineering. In: End-User Software Engineering Dagstuhl Seminar.

Dittrich, Y., 2007. Rethinking the Software Life Cycle: About the Interlace of Different Design and Development Activities. In: End-User Software Engineering Dagstuhl Seminar.

Engels, G., 2007. Model-Driven Development for End-Users, too!? In: End-User Software Engineering Dagstuhl Seminar.

Fischer, G., 2007. Meta-Design: A Conceptual Framework for End-User Software Engineering. In: End-User Software Engineering Dagstuhl Seminar.

Gross, M. D., 2007. Designers Need End-User Software Engineering. In: End-User Software Engineering Dagstuhl Seminar.

Ko, A. J., 2007. Barriers to Successful End-User Programming. In: End-User Software Engineering Dagstuhl Seminar.

Lieberman, H., 2007. End-User Software Engineering Position Paper. In: End-User Software Engineering Dagstuhl Seminar.

Repenning, A., 2007. End-User Design. In: End-User Software Engineering Dagstuhl Seminar.

Rosson, M. B., 2007. Position paper for EUSE 2007 at Dagstuhl. In: End-User Software Engineering Dagstuhl Seminar.

Segal, J., 2007. End-User Software Engineering and Professional End-User Developers. In: End-User Software Engineering Dagstuhl Seminar.

Spahn, M., Scheidl, S., Stoitsev, T., 2007. End-User Development Techniques for Enterprise Resource Planning Software Systems. In: End-User Software Engineering Dagstuhl Seminar.

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