[J17]
@article{cashman:emse2021,
author = {Cashman, Mikaela and Firestone, Justin and Cohen, Myra B. and Thianniwet, Thammasak and Niu, Wei},
doi = {10.1007/s10664-021-09940-0},
journal = {Empirical Software Engineering},
number = {3},
pages = {44},
title = {An Empirical Investigation of Organic Software Product Lines},
volume = {26},
year = {2021}}
Abstract:
Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5{\%} of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 ×1020 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.
[J15]
@article{yu:stvr2018
author = {Yu, Tingting and Srisa-an, Witawas and Cohen, Myra B. and Rothermel, Gregg},
title = {A hybrid approach to testing for nonfunctional faults in embedded systems using genetic algorithms},
journal = {Software Testing, Verification and Reliability},
volume = {28},
number = {7},
pages = {e1686},
doi = {https://doi.org/10.1002/stvr.1686},
year = {2018}}
Abstract:
mbedded systems are challenging to program correctly, because they use an interrupt-driven programming paradigm and run in resource-constrained environments. This leads to various classes of nonfunctional faults that can be detected only by customized verification techniques. These nonfunctional faults are specifically related to usage of resources such as time and memory. For example, the presence of interrupts can induce delays in interrupt servicing and in system execution time. Such delays can occur when multiple interrupt service routines and interrupts of different priorities compete for resources on a given CPU. As another example, stack overflows are caused when the combination of active methods and interrupt invocations on the stack grows too large, and these can lead to data loss and other significant device failures. To detect these types of nonfunctional faults, developers need to estimate worst-case resource usage. Most existing approaches for calculating such estimates are based on static analysis; however, these have a tendency to overapproximate the resources needed. Dynamic techniques such as random testing, in contrast, often underapproximate resource usage. In this article, we present SimEspresso, a framework that uses a combination of static analysis and a test case generation algorithm to estimate worst-case resource usage. There are three different worst-case resource usage scenarios that we consider: (1) worst-case execution times, (2) worst-case interrupt latencies, and (3) worst-case stack usage. SimEspresso first uses static analysis to identify program paths and interrupt interleavings that potentially lead to worst-case scenarios. It then uses a genetic algorithm to generate test cases that guide program execution down these paths, using these particular interrupt interleavings. We performed an empirical study to evaluate the effectiveness of SimEspresso; our results show that SimEspresso is more effective than static analysis approaches and improves significantly over the state of the art dynamic technique, random test case generation. We also find that when we use only the genetic algorithm, omitting the static analysis, SimEspresso performs almost as effectively, but takes significantly longer to complete its task.