Software Engineering

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Search-based software engineering Evidence-based software engineering for practitioners Issues in agent-based software engineering Knowledge management in software engineering

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Search-based software engineering

Search based optimization techniques have been applied to a number of software engineering activities, right across the life-cycle from requirements engineering [5], project planning and cost estimation [1, 3, 4, 16, 28, 52] through testing [6, 7, 11, 14, 15, 36, 39, 54, 61, 87], to automated maintenance [12, 32, 38, 64, 65, 70, 77, 78], service-oriented software engineering [19], compiler optimization [24, 25] and quality assessment [13, 49].

1. The choice of the representation of the problem.
2. The definition of the fitness function.

Apps: Cost estimation allocations in project planning (bin packaging problem) Requirements engineering, Next Release Problem (NRP) Optimizing design decisions Optimizing source code Optimizing test data generation Optimizing test data selection and prioritization Optimizing maintenance and reverse engineering

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Evidence-based software engineering for practitioners

EBSE aims to improve decision making related to software development and maintenance by integrating current best evidence from research with practical experience and human values.

Close gap between research and practice (academia and industry)

5 steps of EBSE

1. Convert a relevant problem or information need into an answerable question.
2. Search the literature for the best available evidence to answer the question.
3. Critically appraise the evidence for its validity, impact, and applicability.
4. Integrate the appraised evidence with practical experience and the customer’s values and circumstances to make decisions about practice.
5. Evaluate performance and seek ways to improve it.

software process improvement:

1. Identify a problem.
2. Propose a technology or procedure to address that problem.
3. Evaluate the proposed technology in a pilot project.
4. If the technology is appropriate, adopt and implement it.
5. Monitor the organization after implementing the new technology.
6. Return to step 1.

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Issues in agent-based software engineering

Agents:

• autonomy: agents encapsulate some state (that is not accessible to other agents), and make decisions about what to do based on this state, without the direct intervention of humans or others;
• reactivity: agents are situated in an environment, (which may be the physical world, a user via a graphical user interface, a collection of other agents, the INTERNET, or perhaps many of these combined), are able to perceive this environment (through the use of potentially imperfect sensors), and are able to respond in a timely fashion to changes that occur in it;
• pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking the initiative;
• social ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language [12], and typically have the ability to engage in social activities (such as cooperative problem solving or negotiation) in order to achieve their goals.

Role based. Each agent only cares about informations associated with it self.

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Knowledge management in software engineering

A software organization’s main asset is its intellectual capital.

Business needs:

• Decreasing time and cost and increasing quality
• Making better decisions.

Knowledge needs

• Acquiring knowledge about new technologies
• Accessing domain knowledge
• Sharing knowledge about local policies and practices.
• Capturing knowledge and knowing who knows what.
• Collaborating and sharing knowledge.