Cleanroom Software Engineering

Mills, Harlan D.; Dyer, M.; Linger, Richard C.

doi:10.1109/ms.1987.231413

Cited by 305 publications

(112 citation statements)

References 4 publications

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“…These findings result from empirical analyses that establish the following results (see [19]): (i) not all software faults are equally likely to cause software failures; (ii) transient failures or whose consequences are not serious to the users' needs are of little practical importance in the operational use of the software. In some programs, empirical analysis find that removing 60% of product defects would only lead to a 3% reliability improvement [16]. Curiously, these are good results for classical SE.…”

Section: Classic Software Engineering Methods Are Not Reliable In Absmentioning

confidence: 95%

Towards an Emergence-Driven Software Process for Agent-Based Simulation

David

Sichman

Coelho

2003

Multi-Agent-Based Simulation II

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Abstract. In this paper we propose an emergence-driven software process for agent-based simulation that clarifies the traceability of micro and macro observations to micro and macro specifications in agent-based models. We use the concept of hyperstructures [1] to illustrate how micro and macro specifications interact in agent-based models, and we show that the reductionism/non-reductionism debate is important to understand the reliability of agent-based simulations. In particular, we show that the effort expended in the verification of agent-based simulations increases exponentially with the number of micro and macro specifications, and that the reliability assessment of non-anticipated results in simulation is in practice not possible. According to these results we claim to be impossible in practice to verify that an agent-based conceptual model has been implemented properly as a computational model, since one does not usually know what is the desired output a priori. We thus advocate that the classic process of verification, validation and exploration of non-anticipated results is not reliable in an agent-based simulation, and call into question the applicability of traditional software engineering methods to agentbased simulation.

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Section: Classic Software Engineering Methods Are Not Reliable In Absmentioning

confidence: 95%

Towards an Emergence-Driven Software Process for Agent-Based Simulation

David

Sichman

Coelho

2003

Multi-Agent-Based Simulation II

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“…Linger introduced a cleanroom software engineering (CSE) for zero-defect software [15]. In the cleanroom process, correctness is built in by the development team through formal specification, design, and verification [16]. Team correctness verification takes the place of unit testing and debugging, and software enters system testing directly, with no execution by the development team.…”

Section: Dm For Zdmmentioning

confidence: 99%

Towards zero-defect manufacturing (ZDM)—a data mining approach

Wang

2013

Adv. Manuf.

110

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The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero-defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.

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“…We assume that the operational profile is already decided at the requirement stage and how to get the operational profile of a system is out of the scope of this paper. The reader can refer to [6,[22][23][24] for details on operational profile. Once both the factors of a class: influence value and average execution time are computed, we can assign test priority to each class based on these two factors.…”

Section: Overview Of Our Approachmentioning

confidence: 99%

Source Code Prioritization Using Forward Slicing for Exposing Critical Elements in a Program

Ray

Kumawat

Mohapatra

2011

J. Comput. Sci. Technol.

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Even after thorough testing, a few bugs still remain in a program with moderate complexity. These residual bugs are randomly distributed throughout the code. We have noticed that bugs in some parts of a program cause frequent and severe failures compared to those in other parts. Then, it is necessary to take a decision about what to test more and what to test less within the testing budget. It is possible to prioritize the methods and classes of an object-oriented program according to their potential to cause failures. For this, we propose a program metric called influence metric to find the influence of a program element on the source code. First, we represent the source code into an intermediate graph called extended system dependence graph. Then, forward slicing is applied on a node of the graph to get the influence of that node. The influence metric for a method m in a program shows the number of statements of the program which directly or indirectly use the result produced by method m. We compute the influence metric for a class c based on the influence metric of all its methods. As influence metric is computed statically, it does not show the expected behavior of a class at run time. It is already known that faults in highly executed parts tend to more failures. Therefore, we have considered operational profile to find the average execution time of a class in a system. Then, classes are prioritized in the source code based on influence metric and average execution time. The priority of an element indicates the potential of the element to cause failures. Once all program elements have been prioritized, the testing effort can be apportioned so that the elements causing frequent failures will be tested thoroughly. We have conducted experiments for two well-known case studies -Library Management System and Trading Automation System -and successfully identified critical elements in the source code of each case study. We have also conducted experiments to compare our scheme with a related scheme. The experimental studies justify that our approach is more accurate than the existing ones in exposing critical elements at the implementation level.

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Cleanroom Software Engineering

Cited by 305 publications

References 4 publications

Towards an Emergence-Driven Software Process for Agent-Based Simulation

Towards an Emergence-Driven Software Process for Agent-Based Simulation

Towards zero-defect manufacturing (ZDM)—a data mining approach

Source Code Prioritization Using Forward Slicing for Exposing Critical Elements in a Program

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