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- Antinyan, Vard, 1984, et al.
(författare)
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Defining technical risks in software development
- 2014
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Ingår i: Joint Conference of the 24th International Workshop on Software Measurement, IWSM 2014 and the 9th International Conference on Software Process and Product Measurement, Mensura 2014; ss RotterdamRotterdam; Netherlands; 6 October 2014 through 8 October 2014. - : IEEE. - 9781479941742
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Konferensbidrag (refereegranskat)abstract
- Challenges of technical risk assessment is difficult to address, while its success can benefit software organizations appreciably. Classical definition of risk as a 'combination of probability and impact of adverse event' appears not working with technical risk assessment. The main reason of this is the nature of adverse event's outcome which is rather continuous than discrete. The objective of this study was to scrutinize different aspects of technical risks and provide a definition, which will support effective risk assessment and management in software development organizations. In this study we defined the risk considering the nature of actual risks, emerged in software development. Afterwards, we summarized the software engineers' view on technical risks as results of three workshops with 15 engineers of four software development companies. The results show that technical risks could be viewed as a combination of uncertainty and magnitude of difference between actual and optimal design of product artifacts and processes. The presented definition is congruent with practitioners view on technical risk. It supports risk assessment in a quantitative manner and enables identification of potential product improvement areas.
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| 2. |
- Antinyan, Vard, 1984, et al.
(författare)
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Identifying risky areas of software code in Agile/Lean software development: An industrial experience report
- 2014
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Ingår i: 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering, CSMR-WCRE 2014 - Proceedings. - : IEEE. - 9781479941742
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Konferensbidrag (refereegranskat)abstract
- Modern software development relies on incremental delivery to facilitate quick response to customers' requests. In this dynamic environment the continuous modifications of software code can cause risks for software developers; when developing a new feature increment, the added or modified code may contain fault-prone or difficult-to-maintain elements. The outcome of these risks can be defective software or decreased development velocity. This study presents a method to identify the risky areas and assess the risk when developing software code in Lean/Agile environment. We have conducted an action research project in two large companies, Ericsson AB and Volvo Group Truck Technology. During the study we have measured a set of code properties and investigated their influence on risk. The results show that the superposition of two metrics, complexity and revisions of a source code file, can effectively enable identification and assessment of the risk. We also illustrate how this kind of assessment can be successfully used by software developers to manage risks on a weekly basis as well as release-wise. A measurement system for systematic risk assessment has been introduced to two companies. © 2014 IEEE.
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| 3. |
- Durisic, Darko, 1986, et al.
(författare)
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Quantifying long-term evolution of industrial meta-models - A case study
- 2014
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Ingår i: Joint Conference of the 24th International Workshop on Software Measurement, IWSM 2014 and the 9th International Conference on Software Process and Product Measurement, Mensura 2014, Rotterdam, Netherlands, 6-8 October 2014. - : IEEE. - 9781479941742
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Konferensbidrag (refereegranskat)abstract
- Measurement in software engineering is an important activity for successful planning and management of projects under development. However knowing what to measure and how is crucial for the correct interpretation of the measurement results. In this paper, we assess the applicability of a number of software metrics for measuring a set of meta-model properties - size, length, complexity, coupling and cohesion. The goal is to identify which of these properties are mostly affected by the evolution of industrial meta-models and also which metrics should be used for their successful monitoring. In order to assess the applicability of the chosen set of metrics, we calculate them on a set of releases of the standardized meta-model used in the development of automotive software systems - the AUTOSAR meta-model - in a case study at Volvo Car Corporation. To identify the most applicable metrics, we used Principal Component Analysis (PCA). The results of these metrics shall be used by software designers in planning software development projects based on multiple AUTOSAR meta-model versions. We concluded that the evolution of the AUTOSAR meta-model is quite even with respect to all 5 properties and that the metrics based on fan-in complexity and package cohesion quantify the evolution most accurately.
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| 4. |
- Rezaei, Hengameh, et al.
(författare)
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Identifying and Managing Complex Modules in Executable Software Design Models - Empirical Assessment of a Large Telecom Software Product
- 2014
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Ingår i: 2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA), Rotterdam, The Netherlands, October 6-8, 2014. - Los Alamitos : IEEE Computer Society. - 9781479941742 ; 1:1
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Konferensbidrag (refereegranskat)abstract
- Using design models instead of executable code has shown itself to be an efficient way of increasing abstraction level of software development. However, applying established code-based software engineering methods to design models can be a challenge - due to different abstraction levels, the same metrics as for code are not applicable for the design models. One of practical challenges in using metrics at the model level is applying complexity-prediction formulas developed using code-based metrics to design models. The existing formulas do not apply as they do not take into consideration the behavior part of the models - e.g. State charts. In this paper we address this challenge by conducting a case study at one of the large telecom products at Ericsson with the goal to identify which metrics can predict complex, hard to understand and hard to maintain software modules based on their design models. We use both statistical methods like regression to build prediction formulas and qualitative interviews to codify expert designers' perception of which software modules are complex. The results of this case study show that such measures as the number of non-self-transitions, transition per states or state depth can be combined in order to identify software units that are perceived as complex by expert designers. Our conclusion is that these metrics can be used in other companies to predict complex modules, but the coefficients should be recalculated per product to increase the prediction accuracy.
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