摘要 :
[Context and motivation] When developing software, coordination between different organizational units is essential in order to develop a good quality product, on time and within budget. Particularly, the synchronization between r...
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[Context and motivation] When developing software, coordination between different organizational units is essential in order to develop a good quality product, on time and within budget. Particularly, the synchronization between requirements and verification processes is crucial in order to assure that the developed software product satisfies customer requirements. [Question/problem] Our research question is: what are the current challenges in aligning the requirements and verification processes? [Principal ideas/results] We conducted an interview study at a large software development company. This paper presents preliminary findings of these interviews that identify key challenges in aligning requirements and verification processes. [Contribution] The result of this study includes a range of challenges faced by the studied organization grouped into the categories: organization and processes, people, tools, requirements process, testing process, change management, traceability, and measurement. The findings of this study can be used by practitioners as a basis for investigating alignment in their organizations, and by scientists in developing approaches for more efficient and effective management of the alignment between requirements and verification.
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During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the perf...
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During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the performance of software activities and to make better software estimates. This has enabled a number of studies with an emphasis on the relationship between software product size, effort and cost drivers in order to either measure the average performance for similar software projects or to develop estimation models and then refine them using the collected data. However, despite these efforts, none of those methods are yet deemed to be universally applicable and there is still no agreement on which cost drivers are significant in the estimation process. This study discusses some of the possible reasons why in software engineering, practitioners and researchers have not yet been able to come up with reasonable and well quantified relationships between effort and cost drivers although considerable amounts of data on software projects have been collected. An improved classification of application types in benchmarking repositories is also proposed.
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摘要 :
During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the perf...
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During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the performance of software activities and to make better software estimates. This has enabled a number of studies with an emphasis on the relationship between software product size, effort and cost drivers in order to either measure the average performance for similar software projects or to develop estimation models and then refine them using the collected data. However, despite these efforts, none of those methods are yet deemed to be universally applicable and there is still no agreement on which cost drivers are significant in the estimation process. This study discusses some of the possible reasons why in software engineering, practitioners and researchers have not yet been able to come up with reasonable and well quantified relationships between effort and cost drivers although considerable amounts of data on software projects have been collected. An improved classification of application types in benchmarking repositories is also proposed.
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摘要 :
With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applica...
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With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients' behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.
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摘要 :
With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applica...
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With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies, In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the end-user, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients' behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.
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摘要 :
Static analysis of source code is one way to find bugs and problems in large software projects. Many approaches to static analysis have been proposed. We proposed a novel way of performing static analysis. Instead of methods based...
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Static analysis of source code is one way to find bugs and problems in large software projects. Many approaches to static analysis have been proposed. We proposed a novel way of performing static analysis. Instead of methods based on semantic/logic analysis we apply machine learning directly to the problem. This has many benefits. Learning by example means trivial programmer adaptability (a problem with many other approaches), learning systems also has the advantage to be able to generalise and find problematic source code constructs that are not exactly as the programmer initially thought, to name a few. Due to the general interest in code quality and the availability of large open source code bases as test and development data, we believe this problem should be of interest to the larger data mining community. In this work we extend our previous approach and investigate a new way of doing feature selection and test the suitability of many different learning algorithms. This on a selection of problems we adapted from large publicly available open source projects. Many algorithms were much more successful than our previous proof-of-concept, and deliver practical levels of performance. This is clearly an interesting and minable problem.
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We first study monomial systems on the n-torus modulo a prime number p. We investigate the dynamics by using group structures and directed graphs. Under certain conditions the systems we get by lifting to the p-adic n-torus inheri...
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We first study monomial systems on the n-torus modulo a prime number p. We investigate the dynamics by using group structures and directed graphs. Under certain conditions the systems we get by lifting to the p-adic n-torus inherits much of the structure of the system modulo p. We also discuss the characters of the periodic points.
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To shorten time-to-market and improve customer satisfaction, software development companies commonly want to use metrics for assessing and improving the performance of their development projects. This paper describes a measurement...
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To shorten time-to-market and improve customer satisfaction, software development companies commonly want to use metrics for assessing and improving the performance of their development projects. This paper describes a measurement concept for assessing how good an organization is at finding faults when most cost-effective, i.e. in most cases early. The paper provides results and lessons learned from applying the measurement concept widely at a large software development company. A major finding was that on average, 64 percent of all faults found would have been more cost effective to find during unit tests. An in-depth study of a few projects at a development unit also demonstrated how to use the measurement concept for identifying which parts in the fault detection process that needs to be improved to become more efficient (e.g. reduce the amount of time spent on rework).
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Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manu...
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Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers. We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot’s ability to assist with workload balancing and the decrease code review lead time.
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摘要 :
Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manu...
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Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers. We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot’s ability to assist with workload balancing and the decrease code review lead time.
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