Planning the software industrial revolution

Cox, Brad

doi:10.1109/52.60587

Cited by 119 publications

(40 citation statements)

References 3 publications

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“…1 Besides potentially increasing productivity and reducing cycle time, the reuse of proven components (especially black-box reuse) should reduce the maintenance burden and lead to more reliable and efficient systems. 2 Although adopting a new software process technology is rarely easy, the early adoption of complex, framebreaking technologies such as OO is likely to be particularly difficult and risky.…”

mentioning

confidence: 99%

Object technology and reuse: lessons from early adopters

Fichman

Kemerer

1997

Computer

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mentioning

confidence: 99%

Object technology and reuse: lessons from early adopters

Fichman

Kemerer

1997

Computer

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“…Cox [9] described this general transformation and argued that because of its relatively recent origins, the software domain has not yet made such a transformation. Agile software development methods have emerged as a practice-led approach that has now become the predominant mode of development in organizations, with estimates that more than 84 percent of organizations are following an agile approach [43].…”

Section: Sir Francis Galtonmentioning

confidence: 99%

Evidence-based decision making in lean software project management

Fitzgerald

Musiał²,

Stol

2014

Companion Proceedings of the 36th International Conference on Software Engineering

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Many professions evolve from their origins as a creative craft process to a more product-centered industrial process. Software development is on such an evolutionary trajectory. A major step in this evolution is the progression from ad hoc to more rigorous evidence-based decision-making in software development project management. This paper extends theory and practice in relation to lean software development using such an evidence-based approach. Based on a comprehensive dataset of software development metrics, gathered in a longitudinal case study over a 22-month period, the Erlang-C model is used to analyze different software development parameters and to guide management decision-making in relation to development resources. The analysis reveals how 'gut-feel' and intuition can be replaced by evidence-based decision-making, and reveals how incorrect assumptions can underpin decisions which as a consequence do not achieve the desired outcome.

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“…a world of were-wolves and silver bullets." This quote from Brad Cox [1] defined the challenges for software project managers in the past as well as today. The software project manager needs to have the skills, techniques and monitoring and control tools to meet the goal of a software development project.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

Sheta¹,

Kassaymeh²,

Rine³

2014

ijacsa

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Abstract-On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

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Planning the software industrial revolution

Cited by 119 publications

References 3 publications

Object technology and reuse: lessons from early adopters

Object technology and reuse: lessons from early adopters

Evidence-based decision making in lean software project management

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

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