Code decay is a gradual process that negatively impacts the quality of a software system. Developers need trusted measurement techniques to evaluate whether their systems have decayed. This paper aims to assess code d...
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ISBN:
(纸本)9781510812277
Code decay is a gradual process that negatively impacts the quality of a software system. Developers need trusted measurement techniques to evaluate whether their systems have decayed. This paper aims to assess code decay by discovering software architectural violations. We propose a methodology that uses Lightweight Sanity Check for Implemented Architectures (LiSCIA) to derive architectural constraints represented by canuse and cannot-use phrases. Our methodology also uses a reverse engineering tool to discover architectural violations. We also introduce measures that indicate code decay in a software system. We conducted a case study of a proprietary system (14 versions) to demonstrate our methodology for assessing code decay. Resulting architectural constraints and architectural violations were validated by the expert of the system. The proposed code decay metrics can give managers datadriven insight into the process of software development, the history of the software product, and the status of unresolved violations. Our results and qualitative analysis showed that the methodology was effective and required a practical level of effort for moderate sized software systems.
Many organisations have turned to crowdsource their software development projects. This raises important pricing questions, a problem that has not previously been addressed for the emerging crowdsourcing development p...
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ISBN:
(纸本)9781467330763
Many organisations have turned to crowdsource their software development projects. This raises important pricing questions, a problem that has not previously been addressed for the emerging crowdsourcing development paradigm. We address this problem by introducing 16 cost drivers for crowdsourced development activities and evaluate 12 predictive pricing models using 4 popular performance measures. We evaluate our predictive models on TopCoder, the largest current crowdsourcing platform for software development. We analyse all 5,910 software development tasks (for which partial data is available), using these to extract our proposed cost drivers. We evaluate our predictive models using the 490 completed projects (for which full details are available). Our results provide evidence to support our primary finding that useful prediction quality is achievable (Pred(30)>0.8). We also show that simple actionable advice can be extracted from our models to assist the 430,000 developers who are members of the TopCoder software development market.
Mutation analysis inserts faults into a program to create test sets that distinguish the mutant from the original program. Inserted faults must represent plausible errors. Standard transformations can mutate scalar va...
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ISBN:
(纸本)076951426X;0769514278
Mutation analysis inserts faults into a program to create test sets that distinguish the mutant from the original program. Inserted faults must represent plausible errors. Standard transformations can mutate scalar values such as integers, floats, and character data. Mutating objects is an open problem, because object semantics are defined by the programmer and can vary widely. We develop mutation operators and support tools that can mutate Java library items that are heavily used in commercial software. Our mutation engine can support reusable libraries of mutation components to inject faults into objects that instantiate items from these common Java libraries. Our technique should be effective for evaluating real-world software testing suites.
Deciding which sub-tool to use for a given proof state requires expertise specific to each interactive theorem prover (ITP). To mitigate this problem, we present PaMpeR, a proof method recommendation system for Isabel...
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ISBN:
(数字)9781450359375
ISBN:
(纸本)9781450359375
Deciding which sub-tool to use for a given proof state requires expertise specific to each interactive theorem prover (ITP). To mitigate this problem, we present PaMpeR, a proof method recommendation system for Isabelle/HOL. Given a proof state, PaMpeR recommends proof methods to discharge the proof goal and provides qualitative explanations as to why it suggests these methods. PaMpeR generates these recommendations based on existing hand-written proof corpora, thus transferring experienced users' expertise to new users. Our evaluation shows that PaMpeR correctly predicts experienced users' proof methods invocation especially when it comes to special purpose proof methods.
Presently, personal data protection and supplementary methods delivering the required level of confidentiality may not fulfil the existing European Union legal standards. Moreover, it still has numerous research gaps....
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ISBN:
(纸本)9798350322446
Presently, personal data protection and supplementary methods delivering the required level of confidentiality may not fulfil the existing European Union legal standards. Moreover, it still has numerous research gaps. Therefore, the most crucial objective demanded by multiple stakeholders is securing the correct application of privacy-enhancing technology (PET) methods concerning General data Protection Regulation (GDPR) compliance. However, no review and analysis of the overlap exist between the PET and compliance domains focusing on security goals and the anonymization level. Consequently, this study focuses on the implications of security goals and data Privacy Impact Assessment (DPIA) introduced in GDPR to enhance personal data security from the respondent's group standpoint, namely data protection officers, software developers, privacy legal practitioners and privacy researchers. Additionally, the consequence of different levels of anonymization, security goals, and PET method (Differential Privacy) concerning appropriate compliance will be studied. More importantly, our research method relies on a literature review and a detailed survey with a specific group of respondents. Therefore, this review provides a snapshot of the current situation between the needs of a particular group of respondents and security goals, with an additional focus on different anonymization levels and compliance requirements.
software fault prediction means identification of the fault-prone parts in the software. This enables to focus testing activities on those software modules that are predicted as fault-prone. As can be seen in literatu...
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software fault prediction means identification of the fault-prone parts in the software. This enables to focus testing activities on those software modules that are predicted as fault-prone. As can be seen in literature, many soft computing techniques are employed to make more accurate predictions previously. However, software fault prediction has not become routine activity in the software development process, because most of employed methods require historical data to train the model. In fact, collection of the historical data is not a simple job and also collected data represents the project which was observed. It may not be reusable for different projects. To overcome these problems, use of Mamdani type fuzzy inference system to predict software fault prone modules is suggested in this study. Another reason is to eliminate the disadvantages sourced from the small size of data. In this study, object oriented metrics are preferred because of widespread use of object oriented technologies. Experimental results show that fuzzy inference systems are successful and can be competitive with methods previously employed in the literature.
Exploratory software testing is an activity which can be carried out by both untrained and formally trained testers. In this paper, we propose using Human Computer Interaction (HCI) techniques to carry out a study of ...
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User feedback and usage data are a valuable source of knowledge for requirements engineering (RE) and software evolution. Recent research in RE has focused on the acquisition and analysis of such user input. However, ...
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ISBN:
(纸本)9781728139128
User feedback and usage data are a valuable source of knowledge for requirements engineering (RE) and software evolution. Recent research in RE has focused on the acquisition and analysis of such user input. However, we argue that important information complementing existing data has been neglected, namely users' emotions shown during app usage, which provide insight into how users experience the app. We are aware that tracking users' emotions at large-scale might be challenging. So far, emotion tracking is often applied in lab and test settings only. We envision an unobtrusive emotion tracker running on the users' smartphones in their regular usage context. However, we do not know whether such an approach would be accepted by the app users and which requirements such an emotion-tracking solution would need to fulfill. In this paper, we present the first results from our exploratory interview study with eight mobile app users regarding their preferences and concerns with respect to emotion tracking. Early results are encouraging and suggest that app users tend to accept emotion tracking during app usage, having only minor privacy concerns. In this paper, we discuss our ongoing research, including the long-term direction and prospects of considering users' emotions for RE.
Well suited data protection has emerged as a critical trouble for data generation. In cloud it is in particular essential due to the fact the information is saved in one of a kind place around the cloud arena. Records...
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softwareengineering-the art of building software-has become a crucial skill in a global economy increasingly affected by IT. This paper tries to indicate where a specific advanced softwareengineering activity takes ...
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