Fitsum Meshesha Kifetew

My Publications

DBLP Page | Google Scholar Page

@article{DBLP:journals/scp/FerdousHKPS23,
  author = {Raihana Ferdous and Chia-kang Hung and Fitsum Meshesha Kifetew and Davide Prandi and Angelo Susi},
  title = {EvoMBT: Evolutionary model based testing},
  journal = {Sci. Comput. Program.},
  year = {2023},
  volume = {227},
  pages = {102942},
  url = {https://doi.org/10.1016/j.scico.2023.102942},
  doi = {https://doi.org/10.1016/J.SCICO.2023.102942}
}

@article{DBLP:journals/stvr/DevroeyGGJKPP23,
  author = {Xavier Devroey and Alessio Gambi and Juan Pablo Galeotti and René Just and Fitsum Meshesha Kifetew and Annibale Panichella and Sebastiano Panichella},
  title = {JUGE: An infrastructure for benchmarking Java unit test generators},
  journal = {Softw. Test. Verification Reliab.},
  year = {2023},
  volume = {33},
  number = {3},
  url = {https://doi.org/10.1002/stvr.1838},
  doi = {https://doi.org/10.1002/STVR.1838}
}

@inproceedings{DBLP:conf/fase/AnsariPPKDDK23,
  author = {Saba Gholizadeh Ansari and I. S. W. B. Prasetya and Davide Prandi and Fitsum Meshesha Kifetew and Mehdi Dastani and Frank Dignum and Gabriele Keller},
  title = {Model-based Player Experience Testing with Emotion Pattern Verification},
  booktitle = {Fundamental Approaches to Software Engineering - 26th International Conference, FASE 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, Paris, France, April 22-27, 2023, Proceedings},
  publisher = {Springer},
  year = {2023},
  volume = {13991},
  pages = {151--172},
  url = {https://doi.org/10.1007/978-3-031-30826-0_9},
  doi = {https://doi.org/10.1007/978-3-031-30826-0%5C_9}
}

@inproceedings{DBLP:conf/icse/FerdousHKPS23,
  author = {Raihana Ferdous and Chia-kang Hung and Fitsum Meshesha Kifetew and Davide Prandi and Angelo Susi},
  title = {EvoMBT at the SBFT 2023 Tool Competition},
  booktitle = {IEEE/ACM International Workshop on Search-Based and Fuzz Testing, SBFT@ICSE 2023, Melbourne, Australia, May 14, 2023},
  publisher = {IEEE},
  year = {2023},
  pages = {59--60},
  url = {https://doi.org/10.1109/SBFT59156.2023.00018},
  doi = {https://doi.org/10.1109/SBFT59156.2023.00018}
}

@incollection{DBLP:series/ncs/KifetewPS23,
  author = {Fitsum Meshesha Kifetew and Anna Perini and Angelo Susi},
  title = {Requirements Engineering},
  booktitle = {Optimising the Software Development Process with Artificial Intelligence},
  publisher = {Springer},
  year = {2023},
  pages = {67--91},
  url = {https://doi.org/10.1007/978-981-19-9948-2_3},
  doi = {https://doi.org/10.1007/978-981-19-9948-2%5C_3}
}

Current & past projects

• iv4XR 2019 - 2022

  iV4XR (intelligent Verification and validation for eXtended Reality). The current XR authoring and development toolset poses no XR testing technology beyond rudimentary record and replay tools that only work for simple test scenarios. iV4XR project (EU H2020, Grant number: 856716) aims to build a novel verification and validation technology for XR systems based on techniques from AI to provide learning and reasoning over a virtual world.

• GAUSS 2017 - 2019

  GAUSS (Governing Adaptive and Unplanned Systems of Systems) is a PRIN project funded by MIUR. The GAUSS project will deliver the methodological enablers required to identify, integrate, and manage “emergent” Systems of Systems (eSoS). These require dynamic and opportunistic engineering due to their intrinsically variable nature tied to their scale and heterogeneity. GAUSS will release a set of integrated technologies to address these engineering problems of eSoS at runtime, when specific execution contexts may invalidate design-time solutions.

• SUPERSEDE 2015 - 2017

  SUPERSEDE (SUpporting evolution and adaptation of PERsonalized Software by Exploiting contextual Data and End-user feedback) is a European H2020 project (n. 644018), funded under the theme ICT-09-2014 (Tools and Methods for Software Development). The SUPERSEDE project proposes a feedback-driven approach for software life cycle management, with the ultimate purpose of improving users’ quality of experience. Decisions on software evolution and runtime adaptation will be made upon analysis of end-user feedback and large amount of data monitored from the context. An integrated platform will articulate the methods and tools produced in the project. The project will provide a new solution framework for software evolution and adaptation for data-intensive applications. Use cases have been identified for different data-intensive application domains: energy consumption, sport event webcasting.

Tools

Here are some research prototypes produced as part of my research.

GitHub

Some of my projects are available from my github page: github.com/kifetew

My contributions in the projects I'm working on could be found in the respective GitHub repositories of the projects:

• iv4XR project: github.com/iv4xr-project
• SUPERSEDE project: github.com/supersede-project

DynaMOSA

"Automated Test Case Generation as a Many-Objective Optimization Problem with Dynamic Selection of the Targets"
by A. Panichella, FM Kifetew, and P. Tonella

The tool implements the DynaMOSA many-objective optimization based approach for test data generation described in our TSE submission, which is currently under review. DynaMOSA extends our previous algorithm MOSA by incorporating dynamic selection of search targets based on structural dependency. It is implemented as an extension of the EvoSuite. The tool and related resoureces could be downloaded below:

Executable jar: EvoSuite-DynaMOSA
Usage(DynaMOSA): java -jar evosuite-dynamosa-xxx.jar -generateMOSuite -Dalgorithm=structuralmosa -cp <path-to-SUT-classpath> -class=class.under.test -Dcriterion=<coverage-criteria>
Usage(MOSA): java -jar evosuite-dynamosa-xxx.jar -generateMOSuite -Dalgorithm=mosa -cp <path-to-SUT-classpath> -class=class.under.test -Dcriterion=<coverage-criteria>
Usage(WholeSuite): java -jar evosuite-dynamosa-xxx.jar -generateSuite -cp <path-to-SUT-classpath> -class=class.under.test -Dcriterion=<coverage-criteria>

<coverage-criteria> could be any one of: branch, statement, strongmutation

The subjects used for the empirical evaluation are selected from the SF110 benchmark, as well as from datasets used in previous works and tool competitions. A replication package, containing the list of classes used in our experiments, as well as the raw results of the experiments, is available: replication_package.zip

Stochastic Grammar Based Testing

A tool that implements the generation of sentences from a senmantically annotated grammar.
Executable jar: GPLib
Usage: java -jar gplib-xxx.jar path-to-annotated-grammar-file max-derivation-tree-depth prob-recursive-prod [opt-num-sentences(defaults to 1)]
Sample annotated grammar: sample grammar
Annotated grammars used in experiments: grammars

Details about the stochastic grammar based sentence generation as well as grammar annotations can be found in the EMSE paper:

"Generating valid grammar-based test inputs by means of genetic programming and annotated grammars".
Fitsum Meshesha Kifetew, Roberto Tiella, and Paolo Tonella.
In Empirical Software Engineering (EMSE), Springer, 2016. Paper

EvoSuite-SVD

Orthogonal diversification of search spaces in evolutionary test case generation
implemented as an extension of the testing tool EvoSuite. To learn more about the tool and download it, please visit the official page. Below is the ISSTA paper describing the details.

Fitsum Meshesha Kifetew, Annibale Panichella, Andrea De Lucia, Rocco Oliveto, and Paolo Tonella. Orthogonal Exploration of the Search Space in Evolutionary Test Case Generation. In International Symposium on Software Testing and Analysis (ISSTA), pp 257-267. ACM, 2013. Paper

Awards & Recognition

• Best paper: SSBSE'18
  Annibale Panichella, Fitsum Meshesha Kifetew, Paolo Tonella: Incremental Control Dependency Frontier Exploration for Many-Criteria Test Case Generation. SSBSE 2018: 309-324
• Distinguished paper: SSBSE'17
  Annibale Panichella, Fitsum Meshesha Kifetew, Paolo Tonella: LIPS vs MOSA: A Replicated Empirical Study on Automated Test Case Generation. SSBSE 2017: 83-98
• Distinguished paper: CAISE'17
  Itzel Morales-Ramirez, Fitsum Meshesha Kifetew, Anna Perini: Analysis of Online Discussions in Support of Requirements Discovery. CAiSE 2017: 159-174
• Best student paper: SSBSE'12
  Fitsum Meshesha Kifetew: A Search-Based Framework for Failure Reproduction. SSBSE 2012: 279-284