| 1. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Evolution of technical debt: An exploratory study
- 2019
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS. - 1613-0073. ; 2476, s. 87-102, s. 87-102
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Konferensbidrag (refereegranskat)abstract
- Context: Technical debt is known to impact maintainability of software. As source code files grow in size, maintainability becomes more challenging. Therefore, it is expected that the density of technical debt in larger files would be reduced for the sake of maintainability. Objective: This exploratory study investigates whether a newly introduced metric ‘technical debt density trend’ helps to better understand and explain the evolution of technical debt. The ‘technical debt density trend’ metric is the slope of the line of two successive ‘technical debt density’ measures corresponding to the ‘lines of code’ values of two consecutive revisions of a source code file. Method: This study has used 11,822 commits or revisions of 4,013 Java source files from 21 open source projects. For the technical debt measure, SonarQube tool is used with 138 code smells. Results: This study finds that ‘technical debt density trend’ metric has interesting characteristics that make it particularly attractive to understand the pattern of accrual and repayment of technical debt by breaking down a technical debt measure into multiple components, e.g., ‘technical debt density’ can be broken down into two components showing mean density corresponding to revisions that accrue technical debt and mean density corresponding to revisions that repay technical debt. The use of ‘technical debt density trend’ metric helps us understand the evolution of technical debt with greater insights.
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| 2. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Correlations of software code metrics : An empirical study
- 2017
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Ingår i: IWSM Mensura '17. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450348539 ; , s. 255-266
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Konferensbidrag (refereegranskat)abstract
- Background: The increasing up-trend of software size brings about challenges related to release planning and maintainability. Foreseeing the growth of software metrics can assist in taking proactive decisions regarding different areas where software metrics play vital roles. For example, source code metrics are used to automatically calculate technical debt related to code quality which may indicate how maintainable a software is. Thus, predicting such metrics can give us an indication of technical debt in the future releases of software. Objective: Estimation or prediction of software metrics can be performed more meaningfully if the relationships between different domains of metrics and relationships between the metrics and different domains are well understood. To understand such relationships, this empirical study has collected 25 metrics classified into four domains from 9572 software revisions of 20 open source projects from 8 well-known companies. Results: We found software size related metrics are most correlated among themselves and with metrics from other domains. Complexity and documentation related metrics are more correlated with size metrics than themselves. Metrics in the duplications domain are observed to be more correlated to themselves on a domain-level. However, a metric to domain level relationship exploration reveals that metrics with most strong correlations are in fact connected to size metrics. The Overall correlation ranking of duplication metrics are least among all domains and metrics. Contribution: Knowledge earned from this research will help to understand inherent relationships between metrics and domains. This knowledge together with metric-level relationships will allow building better predictive models for software code metrics. © 2017 Association for Computing Machinery.
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| 3. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Effects of measurements on correlations of software code metrics
- 2019
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Ingår i: Empirical Software Engineering. - : Springer Science and Business Media LLC. - 1382-3256 .- 1573-7616. ; 24:4, s. 2764-2818
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Tidskriftsartikel (refereegranskat)abstract
- ContextSoftware metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics.ObjectiveSoftware is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics.MethodTo investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall's tau-B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed.ResultsThe cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p <0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak.ConclusionsResults of this study reveal that measurement types may have a significant impact on the correlations of code metrics and that transforming metrics into a different type can give us metrics with low collinearity. These findings provide us a simple understanding how feature transformation to a different measurement type can produce new non-collinear input features for predictive models.
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| 4. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Engineering the Hardware/Software Interface for Robotic Platforms – A Comparison of Applied Model Checking with Prolog and Alloy
- 2013
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Ingår i: Proceedings of the 4th International Workshop on Domain-Specific Languages and models for ROBotic systems.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Robotic platforms serve different use cases ranging from experiments for prototyping assistive applications up to embedded systems for realizing cyber-physical systems in various domains. We are using 1:10 scale miniature vehicles as a robotic platform to conduct research in the domain of self-driving cars and collaborative vehicle fleets. Thus, experiments with different sensors like e.g. ultra-sonic, infrared, and rotary encoders need to be prepared and realized using our vehicle platform. For each setup, we need to configure the hardware/software interface board to handle all sensors and actors. Therefore, we need to find a specific configuration setting for each pin of the interface board that can handle our current hardware setup but which is also flexible enough to support further sensors or actors for future use cases. In this paper, we show how to model the domain of the configuration space for a hardware/software interface board to enable model checking for solving the tasks of finding any, all, and the best possible pin configuration. We present results from a formal experiment applying the declarative languages Alloy and Prolog to guide the process of engineering the hardware/software interface for robotic platforms on the example of a configuration complexity up to ten pins resulting in a configuration space greater than 14.5 million possibilities. Our results show that our domain model in Alloy performs better compared to Prolog to find feasible solutions for larger configurations with an average time of 0.58s. To find the best solution, our model for Prolog performs better taking only 1.38s for the largest desired configuration; however, this important use case is currently not covered by the existing tools for the hardware used as an example in this article.
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| 5. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Explicating, Understanding and Managing Technical Debt from Self-Driving Miniature Car Projects
- 2014
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Ingår i: Proceedings of the 30th IEEE International Conference on Software Maintenance and Evolution (ICSME). - Los Alamitos, CA : IEEE. - 9781479967919
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Konferensbidrag (refereegranskat)abstract
- Technical debt refers to various weaknesses in the design or implementation of a system resulting from trade-offs during software development usually for a quick release. Accumulating such debt over time without reducing it can seriously hamper the reusability and maintainability of the software. The aim of this study is to understand the state of the technical debt in the development of self-driving miniature cars so that proper actions can be planned to reduce the debt to have more reusable and maintainable software. A case study on a selected feature from two self-driving miniature car development projects is performed to assess the technical debt. Additionally, an interview study is conducted involving the developers to relate the findings of the case study with the possible root causes. The result of the study indicates that "the lack of knowledge" is not the primary reason for the accumulation of technical debt from the selected code smells. The root causes are rather in factors like time pressure followed by issues related to software/hardware integration and incomplete refactoring as well as reuse of legacy, third party, or open source code.
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| 6. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Improving Code Smell Predictions in Continuous Integration by Differentiating Organic from Cumulative Measures
- 2019
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Ingår i: The Fifth International Conference on Advances and Trends in Software Engineering. - 2519-8394. - 9781510883741 ; , s. 62-71
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Konferensbidrag (refereegranskat)abstract
- Continuous integration and deployment are enablers of quick innovation cycles of software and systems through incremental releases of a product within short periods of time. If software qualities can be predicted for the next release, quality managers can plan ahead with resource allocation for concerning issues. Cumulative metrics are observed to have much higher correlation coefficients compared to non-cumulative metrics. Given the difference in correlation coefficients of cumulative and noncumulative metrics, this study investigates the difference between metrics of these two categories concerning the correctness of predicting code smell which is internal software quality. This study considers 12 metrics from each measurement category, and 35 code smells collected from 36,217 software revisions (commits) of 242 open source Java projects. We build 8,190 predictive models and evaluate them to determine how measurement categories of predictors and targets affect model accuracies predicting code smells. To further validate our approach, we compared our results with Principal Component Analysis (PCA), a statistical procedure for dimensionality reduction. Results of the study show that within the context of continuous integration, non-cumulative metrics as predictors build better predictive models with respect to model accuracy compared to cumulative metrics. When the results are compared with models built from extracted PCA components, we found better results using our approach.
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| 7. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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MDE-based Sensor Management and Verification for a Self-Driving Miniature Vehicle
- 2013
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Ingår i: 13th ACM Workshop on Domain-Specific Modeling, DSM 2013; Indianapolis, IN; United States. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450326001
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Konferensbidrag (refereegranskat)abstract
- Innovations for today’s vehicle functions are mainly driven by software. They realize comfort systems like automated parking but also safety systems where sensors are continuously monitoring the vehicle's surroundings to brake autonomously for avoiding collisions with cars, pedestrians, or bicyclists. In simulation environments, various traffic situations with alternative sensor setups are imitated before testing them on prototypical cars. In this paper, we are presenting an MDE approach for managing different sensor setups in a cyber-physical system development environment to leverage automated model verification, support system testing, and enable code generation. For example, the models are used as the single point of truth to configure and generate sensor setups for system validations in a 3D simulation environment. After their validation, a considered sensor configuration is transformed into a constraint-satisfaction model to be solved by the logical programming language Prolog. Based on this transformation, the conformance to the embedded system specification is formally verified and possible pin assignments, for how to connect the required sensors are calculated. The approach was validated during the development of a self- driving miniature vehicle using an STM32F4-based embedded system running the real-time operating system ChibiOS as the software/hardware interface to the sensors and actors.
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| 8. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Reducing Simulation Testing Time by Parallel Execution of Loosely Coupled Segments of a Test Scenario
- 2014
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Ingår i: Proceedings of International Workshop on Engineering Simulations for Cyber-Physical Systems. - 9781450326148 ; , s. 5-
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Konferensbidrag (refereegranskat)abstract
- The automotive industry is going through a major transition where all of the main car OEMs (original equipment manufacturer) are putting serious efforts toward self-driving vehicles, and some of the OEMs have the vision to sell fully functional driver-less car by 2020. Software is the primary driving force for implementing different functionalities of today's cyber-physical systems. Being a safety critical system, functionalities of an autonomous vehicle need to be rigorously tested in different driving conditions, for example, weather, traffic, road markings, etc. In order to get early feedback on the implemented functionalities, testing in the simulation environment has become common before testing them on the prototype vehicle. This paper presents an approach to reduce the testing time in the simulation environment by parallelly executing the loosely coupled segments of a test scenario. Reducing simulation testing time is also substantial when we need to run regression test scenarios following changes in the implementation. The preliminary investigation shows that by applying the proposed concept, we can significantly reduce the simulation testing time both in the cases of successful and failed run.
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| 9. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Review and Challenges of Assumptions in Software Development
- 2011
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Ingår i: Second Analytic Virtual Integration of Cyber-Physical Systems Workshop (AVICPS).
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Konferensbidrag (refereegranskat)abstract
- The problems of implicit and invalid assumptions have been identified as one of the key reasons to project and software failures. Assumptions are available in almost all aspects of the software development from human factors to different software development activities. They also have influence on software quality attributes. The aim of this article is to provide a review of existing work in assumption management and find out the assumptions related challenges that should be mitigated in order to build better systems. The results show that assumptions are concerned with many different areas of software engineering and that existing approaches suffer from the lack of scope of assumptions categories and some concerns that are impacted by the assumptions. We believe a holistic assumption management approach can mitigate assumptions related challenges by integrating concerned areas and contribute to build systems with smooth software integration and evolution.
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| 10. |
- Al Mamun, Md Abdullah, 1982, et al.
(författare)
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Towards Formalizing Assumptions on Architectural Level: A Proof-of-Concept
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- While designing an architecture, architects often make assumptions about different factors like execution environment, structural properties of the artifacts, properties of input/output data etc. Implicit and invalid assumptions have been identified as a primary reason for architectural mismatches. Such mismatches cause nightmares to the people working at the system integration phase. Today’s complex systems operate in dynamic and rapidly changing environments. Implicit assumptions in the reusable components often make it challenging to adopt the components in a changed operational domain. If not documented, assumptions are often forgotten and it is both difficult and expensive to find them out from previously built software. This paper aims to formally capture assumptions at the architecture level so that they can be checked automatically throughout the system development. Automated checking of assumptions would facilitate a practicable assumption management system for complex and large-scale software system development with smooth integration and evolution.
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