On the implementation of search based approach to mutation testing

Tuloli, Mohamad Syafri; Sitohang, Benhard; Hendradjaya, Bayu

doi:10.1109/icodse.2017.8285883

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…The mutant generation system is based on our previous mutant generation system based on regular expression (Tuloli, et al, 2016), the system is itself has been proven to be able to use in search-based First-Order Mutant generation (FOM) (Tuloli, et al, 2017). In this research, we explored the usage of this system on generating Second-Order Mutant (SOM).…”

Section: Designmentioning

confidence: 99%

Exploring a Better Searchbased Implementation on Secondorder Mutant Generation

Tuloli

Sitohang

Hendradjaya

2019

JJi

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Pengujian perangkat lunak adalah bagian dari proses pengembangan perangkat lunak, dengan tujuan utama untuk mengurangi/menghilangkan kesalahan pada perangkat lunak, hal ini umumnya dilakukan dengan menjalankan kasus-uji. Salah satu teknik untuk mengkur dan meningkatkan kualitas dari kasus uji adalah pengujian mutasi, tetapi walaupun sudah terbukti keefektifannya, teknik ini masih memiliki suatu kendala besar, yaitu tidak praktis untuk digunakan karena melibatkan pembangkitan dan eksekusi dari jumlah mutan yang besar. Pada penelitian ini, dilakukan eksplorasi penggunaan optimasi berbasispencarian pada pembangkitan mutan (variasi dari program), dengan tujuan untuk menghasilkan mutan yang tidak dapat dideteksi oleh kasus-uji, karena mutan jenis ini memiliki dapat kekurangan dari kasusuji. Metode usulan dibandingkan dengan algoritma pembangkitan second-order mutant yang umum digunakan, dan juga dibandingkan dengan pendekatan berbasis-pencarian lainnya. Hasil menunjukkan bahwa metode usulan dapat membangkitkan lebih banyak mutan-tidak-terdeteksi (undetected-mutant) daripada dengan metode pembangkitan mutan yang umum. Metode usulan memiliki performansi yang lebih rendah daripada metode pembangkitan berbasis-pencarian benchmark, tetapi performansinya dapat ditingkatkan dengan melakukan perubahan pada representasi solusi, dan dengan adopsi parameter optimasi yang digunakan oleh metode pembanding. Kata kunci: analisis mutasi, pengujian mutasi, pengujian perangkat lunak berbasis pencarian, rekayasa perangkat lunak berbasis pencarian

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Section: Designmentioning

confidence: 99%

Exploring a Better Searchbased Implementation on Secondorder Mutant Generation

Tuloli

Sitohang

Hendradjaya

2019

JJi

Self Cite

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“…As shown in Table 5 and Table 6, it is proved that there is a relation between the redundancy level with the coevolution performance. In the Find case has the lowest test case redundancy level ( Table 6, Find case column [4][5][6][7][8][9][10][11][12], and it also has a good performance in test case growth ( Table 5, Find case column 2-4). The relation also shows in mutant solution, the high level of mutant redundancy in BubbleSort, Find and HSL Color (Table 6, column 1-3), correlated with the decrease in mutant growth ( Table 5, column 1), while low redundancy level redundancy in Mid case, related with improved growth.…”

Section: Coevolution Using Laboratory and Real World Casesmentioning

confidence: 99%

“…In this research, we implemented a coevolution method to JMetal library, a java based meta-heuristics library created by Durillo, et.al [6]. We also improved our previous mutant generator [7], [8] by adding a test case generator functionalities. We combined these three functionalities to create a coevolution based mutation testing.…”

Section: Introductionmentioning

confidence: 99%

Coevolution of Second-order-mutant

Tuloli

Sitohang

Hendradjaya

2018

IJECE

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<span>One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.</span>

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On the implementation of search based approach to mutation testing

Cited by 2 publications

References 25 publications

Exploring a Better Searchbased Implementation on Secondorder Mutant Generation

Exploring a Better Searchbased Implementation on Secondorder Mutant Generation

Coevolution of Second-order-mutant

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